Download image links in CSV
Download CSV file with download links for images
The newest applications in continually growing ecosystem
Download CSV file with download links for images
Download images from project or dataset.
serve and use in videos annotator
Converts annotations from Supervisely to COCO format as RLE masks with preserving holes
Interactive evaluation of your instance segmentation model
Transform Supervisely format to YOLOv8 format
Transfer and filter images between Supervisely instances
serve and use in videos annotator
Filter and rank images by text prompts with CLIP models
Run Stable Diffusion model with User Interface
Apply pretrained models for underwater species detection
Deploy model as REST API service
Track polygons, rectangles and points using linear interpolation
Merge Tags in videos or images project
Class-angnostic object detection model
Deploy ClickSEG models for interactive instance segmentation
Deploy model as REST API service
Archive old projects on community
Deploy SelfReformer for salient object segmentation as a REST API service
Deploy model as REST API service
Deploy InSPyReNet for salient object segmentation as a REST API service
Label project images using object segmentor
Downloads and trim video from Youtube.
Downloads images from the Pexels to the dataset.
Deploy Transfiner for instance segmentation as a REST API service
Downloads images from the Flickr to the dataset.
Simple integration of NN training with tensorboard support.
Open metrics in tensorboard
Rotates images along with the annotations in the dataset
Create a new empty project with a meta of original project
Deploy model as REST API service
Label project images using detector and pose estimator
Remove temporary files from Team files
Slice volumes to 2d images
Converts COCO Keypoints format to Supervisely
Evaluate your classification model
Export volume project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Calculate and visualize embeddings
Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view
Import volumes from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Edit tags of each object on image
Binds nested objects into groups
Split one or multiple datasets into parts
Put images with labels into collage and renders comparison videos
This app perspective transforms and warps your images using qr code in them.
Add dataset name tag to all images in project or dataset
Extract video fragment to selected project or dataset
Convert polygon and bitmap labels to semantic segmentation
Import videos by urls provided in text file
Import Volumes with .nrrd masks to Supervisely
Convert all labels in the project or dataset to rotated bounding boxes
Export images in DOTA format and prepares downloadable archive
Puts YouTube logo on all images in directory
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Detailed statistics for all classes in pointcloud or episodes project
Import Pointcloud Project with Annotations and Photo context in Supervisely format
Run Jupyterlab server on your computer with Supervisely Agent and access it from anywhere
Dashboard to configure, start and monitor training
Tag segments (begin and end) on single or multiple videos in dual-panel view
Explore images with certain number of objects of specific class
Converts COCO format to Supervisely
Import images with binary masks as annotations
Filters images and provides results in selected format
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Slicer 3D algorithms for volume interpolation
Export pointclouds project and prepares downloadable tar archive
Export videos project and prepares downloadable tar archive
complete solution for medical DICOM annotation
complete solution for image annotation with advanced features
complete solution for LiDAR annotation with photo context
complete solution for video annotation
complete solution for LiDAR episodes annotation with photo context
complete solution for image annotation
Batched smart labeling tool for Videos
Import selected videos from Team Files to selected destination
Import pointclouds in PCD format without annotations
Import Videos without annotations to Supervisely
serve and use in videos annotator
Creates sequence of connected point clouds with tracklets
NN Inference on videos in project or dataset
Create new object classes from tags associated with objects
Template application to serve custom detection models
Convert each class name to tag associated with objects, and merge existing classes into single one
Run 3D Detection and tracking algorithm on pointclouds or pointcloud episodes project
Deploy model as REST API service
Change video framerate with preserving duration (recodes video)
Label project images using detector and classify predicted boxes
label project images or objects using NN
interactive metrics analysis
Import videos with annotations in Supervisely format
Images with corresponding annotations
Import Supervisely volumes project with annotations
Import volumes in DICOM and NRRD formats without annotations
Import pointclouds without annotations in .ply format from Team Files
Drag and drop images to Supervisely, supported formats: .jpg, .jpeg, jpe, .mpo, .bmp, .png, .tiff, .tif, .webp, .nrrd
Batched smart labeling tool for Images
Deploy model as REST API service
Dashboard to configure, start and monitor training
Convert DICOM data to nrrd format and creates a new project with images grouped by selected metadata
Import images groups connected via user defined tag
Dashboard to configure, start and monitor training
Deploy model as REST API service
Dashboard to configure, start and monitor training
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Import Pointcloud Episodes from KITTI-360 format
Upload images using .CSV file
Analyse videos labeled for Action Recognition task
Label videos for Action Recognition task
Annotate Project using Queues
Label and Review videos for Action Recognition task
Build labels distribution heatmap for dataset.
Deploy model as REST API service
Solve Instance Segmentation tasks
Dashboard to configure, start and monitor training
Calculate embeddings for images project
Google landmarks challenge models
Label images using updatable Reference Database
Recommends matching items from the catalog
Use metric learning models to classify images
Convert .CSV catalog to Images Project
Export project or dataset in Supervisely volumes format
Preview images as a grid gallery
Review images annotations object by object with ease
Export project or dataset in Supervisely pointcloud episode format
Import Pointcloud Episodes with Annotations and Photo context
Converts KITTI 3D format to Supervisely pointcloud format
Converts Supervisely Pointcloud format to KITTI 3D
Creates new project with cropped objects
Deploy interpolation method as REST API service
State of the art object segmentation model in Labeleing Interface
serve and use in videos annotator
Interactive Confusion matrix, mAP, ROC and more
Creates video project from images project
Creates video from images in dataset with selected frame rate and configurable label opacity
Converts Supervisely to COCO format and prepares tar archive for download
Training, inference, data exploration, synthetic data, and more
Deploy model as REST API service
Dashboard to configure, start and monitor training
Synthesize videos on annotated data
Converts shapes of classes on videos (e.g. polygon to bitmap) and all corresponding objects
Import public or custom data in Pascal VOC format to Supervisely
Training, inference, ai-assisted labeling, synthetic data and more
Deploy model as REST API service
Dashboard to configure, start and monitor training
to TorchScript and ONNX formats
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Use neural network in labeling interface to classify images and objects
Saves tag to images mapping to a json file
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool
Filter objects and tags by user and copy them to working area
Image Pixel Classification using ilastik
Download activity as csv file
Import Cityscapes to Supervisely
Invite users to team
images and JSON annotations
Export only labeled items and prepares downloadable tar archive
For semantic and instance segmentation tasks
Merge selected datasets with images or videos into a single one
for both images and their annotations
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Export Images Metadata from Project
Import Metadata for Images in Project
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Converts Supervisely Project to Pascal VOC format
Visualize and build augmentation pipeline with ImgAug
Generate synthetic data for classification of retail products on grocery shelves
Merge images and labels that were split by sliding window before
Configure, preview and split images and annotations with sliding window
All you need to work with YOLOv5
Generate synthetic data: flying foregrounds on top of backgrounds
Assign tags to images using example images
Create foreground mask from alpha channel of image
Deploy model as REST API service
Dashboard to configure and monitor training
Use deployed neural network in labeling interface
NN Inference on images in project or dataset
Review and correct tags (supports multi-user mode)
Supports multi-user mode
Prepare examples for products from catalog
Read every n-th frame and save to images project
Explore images for every combination of tags pairs in co-occurrence table
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
Used to create infinite task for debug
Downloads videos by URLs and uploads them to Supervisely Storage
Visual diff and merge tool helps compare images in two projects
Visual diff and merge tool helps compare project tags and classes
Explore images for every combination of classes pairs in co-occurrence table
Copies images + annotations + images metadata
Creates presentation mp4 file based on labeled video
The number of objects, figures and frames for every class for every dataset
Group items by selected columns from CSV catalog
Creates images project from video project
Objects with specific tag will be treated as reference items
Tags and object classes can be customized
Match image tag with CSV columns and add row values to image
Transform YOLO v5 format to supervisely project
Transform project to YOLO v5 format and prepares tar archive for download
Only instance admin has permissions to run it
template for your headless app