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