Real-time portrait segmentation for mobile devices
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Updated
Jun 17, 2020 - Jupyter Notebook
Real-time portrait segmentation for mobile devices
PyTorch dataset loader for exported Supervisely annotation format, including an example usage tutorial training Faster-RCNN.
This is a simple guide on how to use supervisley!!
Conversion of Dataset from Darknet Format into Supervisely Format via Python.
Performing Instance Segmentation on X-Ray Images with Mask R-CNN
Prediction of COVID-19 infection with Deep Learning using Chest X-Rays, Performing Instance Segmentation with Mask R-CNN.
Convert supervisely output to COCO keypoint data format
Mask R-CNN for metal casting defects detection and instance segmentation using Keras and TensorFlow.
Takes the 'ann' metadata folder of a supervisely dataset and converts all bitmaps to rectangles
Use your yolov5 predictions as supervisely annotations
156 images unlabeled images with road scenery
App signs up users from CSV file. Available only for users with admin permissions or in Enterprise Edition
Copy selected tags from images to objects of selected classes
Reference objects are grouped into batches by columns from CSV catalog
Copy images project from one Supervisely Instance to another (including annotations and images metadata).
Visual diff and merge tool compare projects tags and classes
App allows to extract video frames to images project without labels.
General overview of all labeling jobs in team
Add a description, image, and links to the supervisely topic page so that developers can more easily learn about it.
To associate your repository with the supervisely topic, visit your repo's landing page and select "manage topics."