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