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The latest tools that help you and your team build software better, together.
PascalVOC GT Masks (Sample)
726 sample gt-labeled images
PascalVOC PRED Masks (Sample)
726 sample pred-labeled images
Semantic Segmentation Metrics
interactive metrics analysis
Import videos in Supervisely format
Import videos with annotations in Supervisely format
Import images in Supervisely format
Images with corresponding annotations
Demo volumes with labels
Project with labeled dicom and nrrd volumes
Demo volumes
Demo project with dicom / nrrd volumes without labels
Import Videos from Team Files
Import selected videos from Team Files to selected destination
Import Volumes in Supervisely format
Import Supervisely volumes project with annotations
Import DICOM volumes
Import volumes in DICOM and NRRD formats without annotations
Import Pointclouds PLY
Import pointclouds without annotations in .ply format from Team Files
Import images
Import images without annotations from Team Files
Batched Smart Tool
batched smart labeling tool
Serve MMDetection GPU
Deploy model as REST API service
Train MMDetection GPU
Dashboard to configure, start and monitor training
Import dicom studies
Convert DICOM data to nrrd format and creates a new project with images grouped by selected metadata
Import images groups
Import images groups connected via user defined tag
Train RITM GPU
Dashboard to configure, start and monitor training
Serve MMSegmentation GPU
Deploy model as REST API service
Train MMSegmentation GPU
Dashboard to configure, start and monitor training
EiSeg interactive segmentation SmartTool
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Import KITTI-360
Import Pointcloud Episodes from KITTI-360 format
Import Images from CSV
Upload images using .CSV file
Action Recognition Stats Tool
Analyse videos labeled for Action Recognition task
Action Recognition Labeling Tool
Label videos for Action Recognition task
Classic Labeling Queues
Annotate Project using Queues
Action Recognition
Label and Review videos for Action Recognition task
Labels spatial distribution
Build labels distribution heatmap for dataset.
Serve Detectron2 GPU
Deploy model as REST API service
Instance Segmentation Collection
Solve Instance Segmentation tasks
Train Detectron2 GPU
Dashboard to configure, start and monitor training
Embeddings Calculator
Calculate embeddings for images project
Serve Metric Learning
Google landmarks challenge models
Retail Collection
Label images using updatable Reference Database
AI Recommendations
Recommends matching items from the catalog
Metric Learning Labeling Tool
Use metric learning models to classify images
CSV Products Catalog to Images Project
Convert .CSV catalog to Images Project
Export volumes in Supervisely format
Export project or dataset in Supervisely volumes format
Images thumbnail preview
Preview images as a grid gallery
Objects thumbnail preview
Review images annotations object by object with ease
Export Pointcloud Episodes in Supervisely format
Export project or dataset in Supervisely pointcloud episode format
Import Pointcloud Episodes in Supervisely format
With annotations and photo context
Import KITTI 3D
Converts KITTI 3D format to Supervisely pointcloud format
Export to KITTI 3D
Converts Supervisely Pointcloud format to KITTI 3D
Crop objects on images
Creates new project with cropped objects
3D BBox Interpolation
Deploy interpolation method as REST API service
RITM interactive segmentation SmartTool GPU
State of the art object segmentation model in Labeleing Interface
TransT object tracking (CVPR2021) GPU
serve and use in videos annotator
Object detection metrics
Interactive Confusion matrix, mAP, ROC and more
PascalVOC PRED BBoxes (Sample)
1171 sample prediction-labeled images
PascalVOC GT BBoxes (Sample)
1171 sample gt-labeled images
Images project to videos project
Creates video project from images project
Render video from images
Creates video from images in dataset with selected frame rate and configurable label opacity
Export to COCO
Converts Supervisely to COCO format and prepares tar archive for download
UNet collection
Training, inference, data exploration, synthetic data, and more
Serve UNet GPU
Deploy model as REST API service
Train UNet GPU
Dashboard to configure, start and monitor training
Synthetic videos for tracking
Synthesize videos on annotated data
Convert Video Class Shape
Converts shapes of classes on videos (e.g. polygon to bitmap) and all corresponding objects
Import Pascal VOC
Import public or custom data in Pascal VOC format to Supervisely
Classification collection
Training, inference, ai-assisted labeling, synthetic data and more
Serve MMClassification GPU
Deploy model as REST API service
Train MMClassification GPU
Dashboard to configure, start and monitor training
Export YOLOv5 weights
to TorchScript and ONNX formats
Import images from cloud storage
Import images from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
AI assisted classification
Use neural network in labeling interface to classify images and objects
Tags to image URLs
Saves tag to images mapping to a json file
Unpack key value tags
Rename "Key:Value" tags to key_value (fruit: lemon -> fruit_lemon)
Import LAS/LAZ format
Import LAS/LAZ format files to Supervisely 3D point cloud labeling tool
Review labels side-by-side
Filter objects and tags by user and copy them to working area
ilastik pixel classification
Image Pixel Classification using ilastik
Export activity as csv
Download activity as csv file
Import Cityscapes
Import Cityscapes to Supervisely
Invite users to team from CSV
Invite users to team
Export to Supervisely format
images and JSON annotations
Export only labeled items
Export only labeled items and prepares downloadable tar archive
Export as masks
For semantic segmentation task
Merge datasets
Merge selected datasets with images or videos into a single one
Resize images
for both images and their annotations
Import videos from cloud storage
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Export Metadata
Export Images Metadata from Project
Import Metadata
Import Metadata for Images in Project
Export to Cityscapes
Converts Supervisely annotations to Cityscapes format and prepares downloadable tar archive
Export to Pascal VOC
Converts Supervisely Project to Pascal VOC format
ImgAug Studio
Visualize and build augmentation pipeline with ImgAug
Synthetic retail products
Generate synthetic data for classification of retail products on grocery shelves
Grocery store shelves
Labeled images of products on the shelve: snacks, chips, crisps
Snacks catalog
Labeled images: snacks: chips / crisps / mix
Sliding window merge
Merge images and labels that were split by sliding window before
Sliding window split
Configure, preview and split images and annotations with sliding window
YOLOv5 collection
All you need to work with YOLOv5
Flying objects
Generate synthetic data: flying foregrounds on top of backgrounds
Seeds
Unlabeled images: sunflower / pumpkin (peeled + unpeeled) / mix
Visual Tagging
Assign tags to images using example images
Cats quiz
What breed is this cat? demo for visual tagging app
Top 10 cat breeds
Tag (name of breed) is assigned to every image
Create foreground mask
Create foreground mask from alpha channel of image
Images with alpha channel
Illustrates alpha support in Supervisely
Serve YOLOv5 GPU
Deploy model as REST API service
Train YOLOv5 GPU
Dashboard to configure and monitor training
NN Image Labeling
Use deployed neural network in labeling interface
Apply NN to Images Project
NN Inference on images in project or dataset
Demo Images
17 unlabeled images for quick tests
Review Retail Tags
Review and correct tags (supports multi-user mode)
Retail Tagging
Supports multi-user mode
Mark Reference Objects for Retail
Prepare examples for products from catalog
Extract frames from videos
Read every n-th frame and save to images project
Tags co-occurrence matrix
Explore images for every combination of tags pairs in co-occurrence table
Movie genre from its poster
Application imports kaggle dataset 'Movie genre from its poster' as supervisely project
While True Script
Used to create infinite task for debug
Import videos by URLs from txt file
Downloads videos by URLs and uploads them to Supervisely Storage
Diff and Merge Images Projects
Visual diff and merge tool helps compare images in two projects
Diff and Merge Project Meta
Visual diff and merge tool helps compare project tags and classes
Classes co-occurrence matrix
Explore images for every combination of classes pairs in co-occurrence table
Copy project between instances
Copies images + annotations + images metadata
Render Video Labels to MP4
Creates presentation mp4 file based on labeled video
Video objects stats for every class
The number of objects, figures and frames for every class for every dataset
Group reference objects into batches
Group items by selected columns from CSV catalog
Videos project to images project
Creates images project from video project
Create JSON with reference items
Objects with specific tag will be treated as reference items
Copy image tags to objects
Tags and object classes can be customized
Add properties to image from CSV
Match image tag with CSV columns and add row values to image
Convert YOLO v5 to Supervisely format
Transform YOLO v5 format to supervisely project
Convert Supervisely to YOLO v5 format
Transform project to YOLO v5 format and prepares tar archive for download
Create users from CSV
Only instance admin has permissions to run it
App Template (No GUI)
template for your headless app
Remote import
Connect your remote storage and import data without duplication. Data is stored on your server but visible in Supervisely
Assign train/val tags to images
Assigns tags (train/val) to images. Training apps will use these tags to split data.
Labeler First Time Through
First Time Through ratio shows how many items labeler annotated right the first time (i.e. reviewer accepted his work on first round).
Labeling Events Stats
Total number of labeling actions and annotated unique images in a time interval
Import from Google Cloud Storage
Upload images by reading links (Google Cloud Storage) from CSV file
Labeling Jobs Stats
General statistics for all labeling jobs in team
Create Trainset for SmartTool
Prepare training data for SmartTool
Object Size Stats
Detailed statistics and distribution of object sizes (width, height, area)
Classes stats for images
Detailed statistics for all classes in images project
Merge classes
Merge multiple classes with same shape to a single one
Rasterize objects on images
Convert classes to bitmap and rasterize objects without intersections
Lemons (Annotated)
6 images with annotated lemons and kiwifruits
Lemons (Test)
Sample images project without labels
Print Progress and Error
Prints progress and then raises error
Videos example
Sample videos with labels
Unpack AnyShape Classes
Split "AnyShape" classes to classes with strictly defined shapes (polygon, bitmap, ...)
Convert Class Shape
Converts shapes of classes (e.g. polygon to bitmap) and all corresponding objects
Hello World App
Working demo, use it as a template for your custom apps
Roads (Test)
156 unlabeled images with roads
While True App
nocode app that ignores soft stop
Country Roads
Labeled roads (sample: 100 images, full version: 1000 images)
Persons
Image project with person instances
Roads (Annotated)
10 images with labeled road
Country Roads (Test)
594 unlabeled images
