Web app and cloud service development for the segmentation project described below

For running the production docker do

docker run -it --gpus all -p 8080:8080 pourion/segmentation:latest

On Google Cloud Platform (GCP) you can use the following command in the Console shell to build the Docker image:

$ gcloud builds submit gcr.io/segmentation-service-pam/segment

and then deploy the Docker image by

$ gcloud run deploy - -image gcr.io/segmentation-service-pam/segment

As of now (October 6th) the service is running and available at https://segment-zkkr3aigja-uc.a.run.app/

Image Segmentation with Uncertainty Quantification

The algorithm is based on the paper https://pubmed.ncbi.nlm.nih.gov/31729078/

A U-Net architecture (with MC-Dropout layers) is trained over 4 vision datasets to predict segmentation tasks.

Project Report

A report summarizing implementation and results are provided in report.pdf in the repository.

Development

With Conda

Create the conda environment by conda env create from the repository, the new environment will be built based on environment.yml instructions. Note that the contents of requirements.txt will also be used to build the segment_uq environment.

Do conda activate segment_uq to activate the environment! An advantage of the provided conda environment is it will automatically install the latest version of pytorch v2.0.0.

With Docker (recommended)

The docker environment is provided based on a highly optimized pytorch image from NVIDIA NGC. At this point, an optimized version for pytorch 2 is not yet available on NGC.

Create an account on ngc.nvidia.com and Create an API key and on your machine do docker login nvcr.io, use the provided user/password to login to the ngc server.

To build the docker image ./launch.sh build [ONLY the first time!] and to start it do ./launch.sh dev -d. Then in VS Code Ctrl+Shift+P and choose Attach to Running Container and choose vision from the running containers list.

Instructions

All training and prediction parameters are configured wuth the conf/base.yaml file.
After activating the conda environment with conda activate segment_uq (or using the docker image), train the network with python train.py and generate predicted masks with standard-deviation estimates by python predict.py.

OS/Hardware Requirements

Currently, this codebase is only tested on Linux Ubuntu machine with an RTX 3090 (with 24GB DRAM) or an RTX A6000 GPU (with 48GB DRAM) as well as 64GB of CPU RAM (you need at least 8.5GB DRAM for training on the Pascal dataset).

pourion / segmentation_cloud_service