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Learn how to solve real computer vision tasks from Supervisely experts — top data scientists, web engineers and python developers.
This blog post provides a comprehensive overview of three primary methods of manual image segmentation in Computer Vision: Overlaying, Snap to Object Boundaries and Split Mask.
Learn about the new sidebar that offers quick access to frequently used pages, recent apps and updated team and workspace context switchers.
Complete tutorial on labeling multiple parts of the object using efficient splitting annotation technique in Computer Vision
Guide on video annotation, tagging and management tool tailored for multi-camera videos in Supervisely
We trained Instance Segmentation model for agricultural plant images and achieved remarkable results.
Tutorial on how to use tags and attribute of different types in various industries with real examples.
Learn how to use multi-view display in Supervisely Image Labeling Tool to efficiently annotate multispectral images
A Deep Dive into the Beloved Supervisely Smart Labeling Tools for AI-Assisted Interactive Object Segmentation
Learn how to annotate objects of any complexity by creating freeform outlines using the Brush annotation tool.
Learn how to use Mask Pen annotation tool as a combination of polygonal contours and free-form drawing contours.
This experiment reveals the potential of semi-supervised learning, proving the opportunity to train a model using only a small fraction of labeled data.
Learn how to use Polygon tool for efficient and precise object segmentation in Computer Vision.
How to use modern bounding box image labeling toolbox for Computer Vision in Supervisely.
The complete guide on 3D bounding box interpolation in point cloud episodes in Supervisely.
How to use sliding window method with object detection models to improve accuracy in Computer Vision.
The complete guide on 3D single object tracking on custom point cloud episodes in Supervisely LiDAR annotation toolbox.
Unleash The Power of Domain Adaptation - How to Train Perfect Segmentation Model on Synthetic Data with HRDA
Tutorial on training accurate and robust Semantic Segmentation model without manual annotation using only synthetic training dataset
Discover the optimal approach for annotating and structuring large-scale data labeling tasks with using Labeling Queues and following our step-by-step guide.
We are pleased to share our experience gained from training dozens of models using synthetic data. In this blog post we will provide insights into the training process and present methods to enhance the quality of synthetic data.
Discover the power of collaborative annotation through labeling jobs. Learn how to harness teamwork for efficient and accurate data annotation.
High-quality training datasets with deep analysis and visualization tools.
Get ready to be impressed by how quickly and accurately Supervisely can estimate human poses using advanced techniques.
Discover how to make the most of Optical Character Recognition (OCR) capabilities within Supervisely.
Discovering animal pose estimation with Supervisely. Learn how to annotate animals using keypoints and State-of-the-Art neural networks.
The complete guide on automatic body pose estimation of animals and humans on your images in Supervisely.
We present our synthetic dataset for road surface crack segmentation that was generated automatically, available for research purposes.
Step-by-step guide for industrial inspection cracks segmentation on images using custom interactive AI model.
How to use text prompts and AI to search and query relevant images in your training datasets.
How to use new version of Segment Anything + detailed comparison with original SAM model.
In this ultimate guide and tutorial you will learn what is object tracking and learn how to track objects on your videos with the best models and tools.
How to segment and track objects on videos automatically with Segment Anything and XMem to build custom training datasets.
How to speed up image segmentation in agriculture with custom AI models
Automatically detect anything with only one example using OWL-ViT - one-shot object detection SOTA
The easiest way to get custom YOLOv8 model trained on your own dataset and deploy it with zero coding in the browser.
Learn how to label CT, MRI, and PET medical images for your computer vision models.
How to use modern AI object tracking and segmentation tools to get medical training data 10x faster
Consensus with detailed reports allows you to monitor and prevent systematic inconsistencies during the labeling process