Video labeling tool
complete solution for video annotation
complete solution for video annotation
Clone project or dataset to selected workspace or project, works with all project types: images / videos / 3d / dicom
Import Videos without annotations to Supervisely
Export videos project and prepares downloadable tar archive
Export only labeled items and prepares downloadable tar archive
Dashboard to configure and monitor training
Import videos from cloud (Google Cloud Storage, Amazon S3, Microsoft Azure, ...)
Deploy model as REST API service
Import videos with annotations in Supervisely format
Creates images project from video project
State-of-the art object segmentation model in Labeling Interface
Dashboard to configure, start and monitor training
Deploy model as REST API service
Training dashboard for mmdetection framework (v3.0.0 and above).
Drag and drop interface for building custom DataOps pipelines
Downloads videos by URLs and uploads them to Supervisely Storage
Dashboard to configure, start and monitor training
Read every n-th frame and save to images project
Predictions on every frame are combined with BoT-SORT/DeepSort into tracks automatically
Deploy ClickSEG models for interactive instance segmentation
Dashboard to configure, start and monitor training
Dashboard to configure, start and monitor training
Deploy SAM 2 model as REST API service
Deploy model as REST API service
Deploy model as REST API service
serve and use in videos annotator
Dashboard to configure, start and monitor training
Split one or multiple datasets into parts
The number of objects, figures and frames for every class for every dataset
Run HQ-SAM and then use in labeling tool
Deploy MMDetection 3.0 model as a REST API service
Deploy model as REST API service
Semi-supervised, works with both long and short videos
Creates presentation mp4 file based on labeled video
Tracking settings for video annotation tool
CVPR2022 SOTA video object tracking
Interactive Confusion matrix, mAP, ROC and more
Tag segments (begin and end) with custom attributes on single or multiple videos in dual-panel view
Export items after the passing labeling job review
Apply NN models to video frames
Train HRDA model for segmentation in semi-supervised mode
Deploy model as REST API service
Creates video project from images project
App to obscure data on images and videos
Label videos for Action Recognition task
to TorchScript and ONNX formats
Creates video from images in dataset with selected frame rate and configurable label opacity
Change video framerate with preserving duration (recodes video)
Batched smart labeling tool for Videos
SmartTool integration of Efficient Interactive Segmentation (EISeg)
Annotate Project using Queues
Put images with labels into collage and renders comparison videos
Extract video fragment to selected project or dataset
Export project to Google Cloud Storage, Amazon S3, Microsoft Azure, ...
Tag segments (begin and end) on single or multiple videos in dual-panel view
Track points and polygons on videos
Track polygons, rectangles and points using linear interpolation
Dashboard for SAM 2.1 fine-tuning
Import videos by urls provided in text file
Deploy HRDA model for inference
Track points, polygons and skeletons (keypoints) on videos
Downloads and trim video from Youtube.
Convert and copy multiple Labelbox projects into Supervisely at once.
Import images and videos with annotations in CVAT format.
Effortlessly track and interpolate labeled objects on a conveyor belt in real-time
Merge Tags in videos or images project
Compare annotations of multiple labelers
Track points and polygons on videos
Validate annotations in a project
Analyse videos labeled for Action Recognition task
Converts shapes of classes on videos (e.g. polygon to bitmap) and all corresponding objects
Import selected videos from Team Files to selected destination
Create a new empty project with a meta of original project
serve and use in videos annotator
Mouse action recognition with RT-DETRv2 and MVD
Deploy InSPyReNet for salient object segmentation as a REST API service
Import images and videos with annotations in V7 format.
to TorchScript and ONNX formats
Convert and copy multiple V7 datasets into Supervisely at once.
Deploy MCITrack as REST API service
Transcode videos to mp4 format
Convert and copy multiple CVAT projects into Supervisely at once.
Synthesize videos on annotated data
Train your own models on custom data for mouse action recognition with MVD
Deploy Transfiner for instance segmentation as a REST API service
Prepares training data to use in train mouse action recognition app
Deploy SelfReformer for salient object segmentation as a REST API service
Label and Review videos for Action Recognition task
Sample videos with labels
Video pairs for multicamera labeling