Apply Florence-2 to Images Project
Text-Prompted Object Detection with Mask Segmentation
The newest applications in continually growing ecosystem
Text-Prompted Object Detection with Mask Segmentation
Deploy Florence-2 as a REST API service
Train RT-DETRv2 model on your data
Deploy RT-DETRv2 as a REST API service
Real-time CLAHE filter visualization
Propagate bboxes to multiview images group
Transcode videos to mp4 format
Train RT-DETR model on your data
Deploy RT-DETR as a REST API service
Effortlessly track and interpolate labeled objects on a conveyor belt in real-time
Automated real-time annotation tests.
Evaluate the performance of the NN model and compare it with the results of other models
Deploy SAM 2 model as REST API service
Validate annotations in a project
Allows you to review annotation results in a user interface specifically designed for such tasks
Tracking settings for video annotation tool
Application that visualizes the most recently updated images
Deploy Matte Anything as REST API service
Export Pointclouds and Pointcloud episodes to ROS Bag format
Deploy MMDetection3D models to detect objects in Point Clouds
Train MMDetection3D for detection on Point Clouds data
Apply NN models to video frames
to TorchScript and ONNX formats
App to obscure data on images and videos
Get instant DatasetNinja statistics for your project
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Delete unused projects or their datasets in large batches
Import multispectral images as channels or as separate images.
Convert and copy multiple V7 datasets into Supervisely at once.
Import images and videos with annotations in V7 format.
Dashboard to configure, start and monitor YOLOv5 2.0 training
Deploy YOLOv5 2.0 as REST API service
[Beta] Drag and drop interface for building custom DataOps pipelines
Convert and copy multiple Labelbox projects into Supervisely at once.
Convert and copy multiple Roboflow projects into Supervisely at once.
Service to render annotations on the fly and show them in Supervisely
Track points, polygons and skeletons (keypoints) on videos
Compare annotations of multiple labelers
Sample images from project with different methods
Evaluate your classification model in Detector + Classifier Pipeline
Deploy MBPTrack as REST API service
Convert and copy multiple CVAT projects into Supervisely at once.
Import images and videos with annotations in CVAT format.
Copy team from one Supervisely Instance to another (including workspaces, team members and team files)
Converts Supervisely format to COCO Keypoints
Merge multiple image projects into a single one
Text Detection and Recognition on images
Deploy HRDA model for inference
Train HRDA model for segmentation in semi-supervised mode
Run HQ-SAM and then use in labeling tool
Drag and drop PDFs to import pages as images to Supervisely
Semi-supervised, works with both long and short videos
Used to create infinite task for debug
Deploy MMDetection 3.0 model as a REST API service
Training dashboard for mmdetection framework (v3.0.0 and above).
Deploy YOLOv8 | v9 | v10 | v11 as REST API service
Dashboard to configure, start and monitor YOLOv8 | v9 | v10 | v11 training
CVPR2022 SOTA video object tracking
Compare annotations of multiple labelers
Export items after the passing labeling job review
App for creating and managing annotation exams
Import image projects in Supervisely format from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Download CSV file with download links for images
Download images from project or dataset.
Track points and polygons on videos
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 assets(images) between Supervisely instances
Track points and polygons on videos
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
Class-agnostic interactive detection for auto-prelabeling
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 3D 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 Point Cloud 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, ...
ITK 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
Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically
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
Creates project with images grouped by selected metadata, converting DICOM data to NRRD format in the process.
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
To Supervisely format, compatible with 3D Slicer, MITK
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 Labeling 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