🎉 UNet collection for semantic segmentation

Overview

🚀 This collection is designed to cover semantic segmenetation task in Supervisely and has two main purposes:

1. Demonstrate apps that can be useful for training semantic segmentation models
2. Provide UNet train / serve apps that can be used as a templates for integration of custom segmentation neural network

Before using these apps we recommend to try end-to-end working demo - data and explanations are provided, get more details from apps readmes and how-to videos.

Table of Contents

1. About Supervisely
2. Prerequisites
3. Apps Collection for Semantic Segmentation
   • Demo data and synthetic data
   • Data Exploration
   • Data manipulation - convert, merge, rasterize
   • UNet
   • Integration into labeling tool
4. Contact & Questions & Suggestions

🔥 About Supervisely

You can think of Supervisely as an Operating System available via Web Browser to help you solve Computer Vision tasks. The idea is to unify all the relevant tools that may be needed to make the development process as smooth and fast as possible.

More concretely, Supervisely includes the following functionality:

• Data labeling for images, videos, 3D point cloud and volumetric medical images (dicom)
• Data visualization and quality control
• State-Of-The-Art Deep Learning models for segmentation, detection, classification and other tasks
• Interactive tools for model performance analysis
• Specialized Deep Learning models to speed up data labeling (aka AI-assisted labeling)
• Synthetic data generation tools
• Instruments to make it easier to collaborate for data scientists, data labelers, domain experts and software engineers

One challenge is to make it possible for everyone to train and apply SOTA Deep Learning models directly from the Web Browser. To address it, we introduce an open sourced Supervisely Agent. All you need to do is to execute a single command on your machine with the GPU that installs the Agent. After that, you keep working in the browser and all the GPU related computations will be performed on the connected machine(s).

Prerequisites

You should connect computer with GPU to your Supervisely account. If you already have Supervisely Agent running on your computer, you can skip this step.

Several tools have to be installed on your computer:

• Nvidia drives + CUDA Toolkit
• Docker
• NVIDIA Container Toolkit

Once your computer is ready just add agent to your team and execute automatically generated running command in terminal. Watch how-to video:

🎉 UNet apps collection

To learn more about how to use every app, please go to app's readme page (links are provided). Just add the apps to your team to start using them.

Collection consists of the following apps (find more apps in Ecosystem):

Demo data and synthetic data

• Lemons Annotated - 6 images with two labeled classes: lemons and kiwis

  

• Lemons (Test) - images with products on shelves, will be used to test classification model on real data

  

• Flying objects - app generates synthetic images for segmentation / detection / instance segmentation tasks from labeled examples

  

Data Exploration

• Classes stats for images - classes stats and detailed per image stats

  

• Object size stats - classes stats and detailed per image stats

  

• Classes co-occurrence matrix - discover edge cases and find “suspicious annotations”

  

Data Manipulation

• Convert class shape

  

• Merge classes

  

• Rasterize objects on images

  

• Export as masks

  

UNet apps

• ImgAug studio - prepare and visualize custom augmentation pipeline for training, augs are applied on the fly during training

  

• Train UNet - training dashboard - customize hyperparameters and monitor metrics in real time

  

• Serve UNet - deploy your model as Rest-API service and connect it with other apps from Ecosystem

  

Integration into labeling tool

• Apply NN to images project - app allows to play with different inference options and visualize predictions in real time. Once you choose inference settings you can apply model to all images in your project to visually analyse predictions and perform automatic data pre-labeling.

  

• NN Image Labeling - integrate any deployd NN to Supervisely Image Labeling UI. Configure inference settings and model output classes. Press Apply button (or use hotkey) and detections with their confidences will immediately appear on the image.

  

Contact & Questions & Suggestions

• for technical support please leave issues, questions or suggestions in our repo. Our team will try to help.
• also we can chat in community slack channel or in private workspaces if Enterprise Edition
• if you are interested in Supervisely Enterprise Edition (EE) please send us a request or email Yuri Borisov at sales@supervise.ly