Import Images
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
Apps help you extend functionality with custom UI
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
Import Videos without annotations to Supervisely
Transform project to YOLO v5 format and prepares tar archive for download
Import pointclouds in PCD format without annotations
Converts Supervisely to COCO format and prepares tar archive for download
Export videos project and prepares downloadable tar archive
Transform YOLO v5 format to supervisely project
Transform Supervisely format to YOLOv8 format
Export only labeled items and prepares downloadable tar archive
Export pointclouds project 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.
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Merge selected datasets with images or videos into a single one
Converts COCO format to Supervisely
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Download activity as csv file
Deploy model as REST API service
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Import videos with annotations in Supervisely format
Import Pointcloud Episodes with Annotations and Photo context
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
Import volumes in DICOM and NRRD formats without annotations
Detailed statistics for all classes in images project
Import pointclouds without annotations in .ply format from Team Files
Export project or dataset in Supervisely pointcloud episode format
Downloads videos by URLs and uploads them to Supervisely Storage
Deploy model as REST API service
Training dashboard for mmdetection framework (v3.0.0 and above).
Dashboard to configure, start and monitor training
Read every n-th frame and save to images project
Merge multiple classes with same shape to a single one
[Beta] Drag and drop interface for building custom DataOps pipelines
Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically
Deploy ClickSEG models for interactive instance segmentation
Import public or custom data in Pascal VOC format to Supervisely
Run 3D Detection and tracking algorithm on pointclouds or pointcloud episodes project
Dashboard to configure, start and monitor training
To Supervisely format, compatible with 3D Slicer, MITK
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
Used to create infinite task for debug
Converts Supervisely Pointcloud format to KITTI 3D
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Prepare training data for SmartTool
Deploy model as REST API service
Upload images by reading links (Google Cloud Storage) from CSV file
Export images in DOTA format and prepares downloadable archive
Deploy model as REST API service
Import Point Cloud Project with Annotations and Photo context in Supervisely format
serve and use in videos annotator
Copies images + annotations + images metadata
Deploy model as REST API service
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
Generate synthetic data: flying foregrounds on top of backgrounds
Class-agnostic interactive detection for auto-prelabeling
Deploy model as REST API service
Run HQ-SAM and then use in labeling tool
The number of objects, figures and frames for every class for every dataset
Convert .CSV catalog to Images Project
Batched smart labeling tool for Images
Split one or multiple datasets into parts
Creates presentation mp4 file based on labeled video
Deploy MMDetection 3.0 model as a REST API service
Configure, preview and split images and annotations with sliding window
Semi-supervised, works with both long and short videos
Creates new project with cropped objects
label project images or objects using NN
Download CSV file with download links for images
Deploy SAM 2 model as REST API service
Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere
CVPR2022 SOTA video object tracking
Visual diff and merge tool helps compare images in two projects
Class-angnostic object detection model
Visual diff and merge tool helps compare project tags and classes
Tracking settings for video annotation tool
Deploy model as REST API service
Detailed statistics and distribution of object sizes (width, height, area)
Converts KITTI 3D format to Supervisely pointcloud format
Import Supervisely volumes project with annotations
for both images and their annotations
Import Cityscapes to Supervisely
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Convert classes to bitmap and rasterize objects without intersections
Import Metadata for Images in Project
Image Pixel Classification using ilastik
Dashboard to configure, start and monitor training
Deploy model as REST API service
Interactive Confusion matrix, mAP, ROC and more
Evaluate the performance of the NN model and compare it with the results of other models
General statistics for all labeling jobs in team
Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view
Google landmarks challenge models
Label project images using detector and pose estimator
Calculate embeddings for images project
Recommends matching items from the catalog
Calculate and visualize embeddings
Train HRDA model for segmentation in semi-supervised mode
App for creating and managing annotation exams
Review images annotations object by object with ease
Export items after the passing labeling job review
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
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Import multiview image groups connected via user defined tag
Deploy model as REST API service
Apply NN models to video frames
Creates video project from images project
Interactive evaluation of your instance segmentation model
Edit tags of each object on image
Label project images using detector and classify predicted boxes
Merge multiple image projects into a single one
Convert polygon and bitmap labels to semantic segmentation
interactive metrics analysis
Deploy interpolation method as REST API service
Get instant DatasetNinja statistics for your project
Downloads images from the Pexels to the dataset.
Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool
Explore images with certain number of objects of specific class
Creates sequence of connected point clouds with tracklets
Archive old projects on community
App to obscure data on images and videos
Filter objects and tags by user and copy them to working area
Objects with specific tag will be treated as reference items
Convert and copy multiple Roboflow projects into Supervisely at once.
Filter and rank images by text prompts with CLIP models
Rotates images along with the annotations in the dataset
ITK algorithms for volume interpolation
Label videos for Action Recognition task
to TorchScript and ONNX formats
Convert all labels in the project or dataset to rotated bounding boxes
Evaluate your classification model
Service to render annotations on the fly and show them in Supervisely
Batched smart labeling tool for Videos
Used to create infinite task for debug
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
Change video framerate with preserving duration (recodes video)
Put images with labels into collage and renders comparison videos
Annotate Project using Queues
Dashboard to configure, start and monitor YOLOv5 2.0 training
Converts COCO Keypoints format to Supervisely
Preview images as a grid gallery
Extract video fragment to selected project or dataset
Merge images and labels that were split by sliding window before
Tag segments (begin and end) on single or multiple videos in dual-panel view
Track points and polygons on videos
Import Pointcloud Episodes from KITTI-360 format
Import Volumes with .nrrd 3D Masks to Supervisely
Add dataset name tag to all images in project or dataset
Build labels distribution heatmap for dataset.
Prints progress and then raises error
Binds nested objects into groups
Import volumes from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Track polygons, rectangles and points using linear interpolation
Create foreground mask from alpha channel of image
Export Pointclouds and Pointcloud episodes to ROS Bag format
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Remove temporary files from Team files
Import videos by urls provided in text file
Compare annotations of multiple labelers
Total number of labeling actions and annotated unique images in a time interval
Import multispectral images as channels or as separate images.
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Deploy HRDA model for inference
Downloads and trim video from Youtube.
Text Detection and Recognition on images
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
Deploy MBPTrack as REST API service
Drag and drop PDFs to import pages as images to Supervisely
Explore images for every combination of tags pairs in co-occurrence table
Train RT-DETR model on your data