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 Supervisely format to YOLOv8 format
Transform YOLO v5 format to supervisely project
Export only labeled items and prepares downloadable tar archive
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Dashboard to configure and monitor training
Upload images using .CSV file
Import images with binary masks as annotations
Converts Supervisely Project to Pascal VOC format
Download images from project or dataset.
Merge selected datasets with images or videos into a single one
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Download activity as csv file
Converts COCO format to Supervisely
Deploy model as REST API service
Creates images project from video project
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.
Creates project with images grouped by selected metadata, converting DICOM data to NRRD format in the process.
Dashboard to configure, start and monitor training
Use metric learning models to classify images
Filters images and provides results in selected format
Deploy model as REST API service
Detailed statistics for all classes in images project
Training dashboard for mmdetection framework (v3.0.0 and above).
Drag and drop interface for building custom DataOps pipelines
Merge multiple classes with same shape to a single one
Dashboard to configure, start and monitor training
Read every n-th frame and save to images project
Deploy ClickSEG models for interactive instance segmentation
Converts Supervisely format to COCO Keypoints
Import public or custom data in Pascal VOC format to Supervisely
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Use neural network in labeling interface to classify images and objects
Deploy SAM 2 model as REST API service
Deploy model as REST API service
Prepare training data for SmartTool
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Deploy model as REST API service
Export images in DOTA format and prepares downloadable archive
Upload images by reading links (Google Cloud Storage) from CSV file
Copies images + annotations + images metadata
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Class-agnostic interactive detection for auto-prelabeling
Visualize and build augmentation pipeline with ImgAug
Dashboard to configure, start and monitor training
Split one or multiple datasets into parts
Run HQ-SAM and then use in labeling tool
Deploy MMDetection 3.0 model as a REST API service
Generate synthetic data: flying foregrounds on top of backgrounds
Configure, preview and split images and annotations with sliding window
Download CSV file with download links for images
Deploy model as REST API service
Convert .CSV catalog to Images Project
Batched smart labeling tool for Images
label project images or objects using NN
Creates new project with cropped objects
Class-angnostic object detection model
Visual diff and merge tool helps compare images in two projects
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Deploy model as REST API service
Evaluate the performance of the NN model and compare it with the results of other models
Visual diff and merge tool helps compare project tags and classes
for both images and their annotations
Detailed statistics and distribution of object sizes (width, height, area)
Deploy model as REST API service
Interactive Confusion matrix, mAP, ROC and more
Convert classes to bitmap and rasterize objects without intersections
Import Cityscapes to Supervisely
Import Metadata for Images in Project
Google landmarks challenge models
Image Pixel Classification using ilastik
Calculate embeddings for images project
Label project images using detector and pose estimator
Recommends matching items from the catalog
Export items after the passing labeling job review
Calculate and visualize embeddings
Train HRDA model for segmentation in semi-supervised mode
Assign tags to images using example images
Review images annotations object by object with ease
Deploy model as REST API service
Downloads images from the Pexels to the dataset.
Label project images using detector and classify predicted boxes
Interactive evaluation of your instance segmentation model
Export Images Metadata from Project
Explore images for every combination of classes pairs in co-occurrence table
Edit tags of each object on image
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Merge multiple image projects into a single one
Create new object classes from tags associated with objects
Import multiview image groups connected via user defined tag
Creates video project from images project
Convert polygon and bitmap labels to semantic segmentation
App to obscure data on images and videos
interactive metrics analysis
Convert and copy multiple Roboflow projects into Supervisely at once.
Rotates images along with the annotations in the dataset
Explore images with certain number of objects of specific class
Filter objects and tags by user and copy them to working area
Merge images and labels that were split by sliding window before
Filter and rank images by text prompts with CLIP models
Objects with specific tag will be treated as reference items
Evaluate your classification model
Convert all labels in the project or dataset to rotated bounding boxes
to TorchScript and ONNX formats
Creates video from images in dataset with selected frame rate and configurable label opacity
Label project images using object segmentor
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Put images with labels into collage and renders comparison videos
Add dataset name tag to all images in project or dataset
Dashboard to configure, start and monitor YOLOv5 2.0 training
Converts COCO Keypoints format to Supervisely
Preview images as a grid gallery
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Build labels distribution heatmap for dataset.
Deploy RT-DETRv2 as a REST API service
Train RT-DETRv2 model on your data
Binds nested objects into groups
Dashboard for SAM 2.1 fine-tuning
Create foreground mask from alpha channel of image
Compare annotations of multiple labelers
Dashboard to configure, start and monitor training
Deploy HRDA model for inference
Drag and drop PDFs to import pages as images to Supervisely
Import multispectral images as channels or as separate images.
Text Detection and Recognition on images
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
No description available
Generate synthetic data for classification of retail products on grocery shelves
Saves tag to images mapping to a json file
Convert and copy multiple Labelbox projects into Supervisely at once.
Train RT-DETR model on your data
Match image tag with CSV columns and add row values to image
Import images and videos with annotations in CVAT format.
Effortlessly track and interpolate labeled objects on a conveyor belt in real-time
Explore images for every combination of tags pairs in co-occurrence table
Train YOLO models on your data
Merge Tags in videos or images project
Deploy YOLOv5 2.0 as REST API service
Prepare examples for products from catalog
Sample images from project with different methods
Transfer and filter assets(images) between Supervisely instances
Import image projects in Supervisely format from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Validate annotations in a project
Tags and object classes can be customized
This app perspective transforms and warps your images using qr code in them.
Text-Prompted Object Detection with Mask Segmentation
Automated real-time annotation tests.
Deploy model as REST API service
Deploy Florence-2 as a REST API service
Create a new empty project with a meta of original project
Slice volumes to 2d images
Run Stable Diffusion model with User Interface
Convert each class name to tag associated with objects, and merge existing classes into single one
Allows you to review annotation results in a user interface specifically designed for such tasks
Propagate bboxes to multiview images group
Train DEIM model on your data
Deploy InSPyReNet for salient object segmentation as a REST API service
Import images and videos with annotations in V7 format.
to TorchScript and ONNX formats
Downloads images from the Flickr to the dataset.
Convert and copy multiple V7 datasets into Supervisely at once.
Convert and copy multiple CVAT projects into Supervisely at once.
Deploy RT-DETR as a REST API service
Deploy YOLO models as a REST API service
Export images project to extended Supervisely format with Blobs
Apply pretrained models for underwater species detection
Evaluate your classification model in Detector + Classifier Pipeline
Deploy model as REST API service
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
Deploy Matte Anything as REST API service
Application that visualizes the most recently updated images
Deploy Grounding DINO as a REST API service
Supports multi-user mode
Deploy Kosmos-2 as a REST API service
Deploy Transfiner for instance segmentation as a REST API service
Merge multispectral images into one
Deploy SelfReformer for salient object segmentation as a REST API service
Deploy DEIM as a REST API service
Review and correct tags (supports multi-user mode)
Deploy Molmo as a REST API service
Real-time CLAHE filter visualization
Real-time colormap postprocessing visualization for Image Labeling Toolbox
6 images with annotated lemons and kiwifruits
Image project with person instances
Sample images project without labels