smartbay-prawn-burrow-detection

AI-based Prawn Burrow detection and classification algorithm based on YOLOv8. The model has been tuned to detect and classify Prawn Burrows in Irish Prawn (Nephrops norvegicus) Fishery Areas.

This is a container that will run the smartbay-prawn-burrow-detection application leveraging the DEEP as a Service API component (DEEPaaS API). The application is based on ai4oshub/ai4os-yolov8-torch module.

Running the container

Directly from Docker Hub

To run the Docker container directly from Docker Hub and start using the API, simply run the following command:

$ docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 ai4oshub/smartbay-prawn-burrow-detection

This command will pull the Docker container from the Docker Hub ai4oshub repository and start the default command (deepaas-run --listen-ip=0.0.0.0).

N.B. For either CPU-based or GPU-based images you can also use udocker.

Building the container

If you want to build the container directly in your machine (because you want to modify the Dockerfile for instance) follow the following instructions:

git clone https://github.com/ai4os-hub/smartbay-prawn-burrow-detection
cd smartbay-prawn-burrow-detection
docker build -t ai4oshub/smartbay-prawn-burrow-detection .
docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 ai4oshub/smartbay-prawn-burrow-detection

These three steps will download the repository from GitHub and will build the Docker container locally on your machine. You can inspect and modify the Dockerfile in order to check what is going on. For instance, you can pass the --debug=True flag to the deepaas-run command, in order to enable the debug mode.

Connect to the API

Once the container is up and running, browse to http://0.0.0.0:5000/ui to get the OpenAPI (Swagger) documentation page.

Project structure

├─ Dockerfile             <- Describes main steps on integration of DEEPaaS API and
│                            <your_project> application in one Docker image
│
├─ Jenkinsfile            <- Describes basic Jenkins CI/CD pipeline
│
├─ LICENSE                <- License file
│
├─ README.md              <- README for developers and users
│
├── .sqa/                 <- CI/CD configuration files
│
└─ ai4-metadata.yml       <- Defines information propagated to the AI4OS Hub

You can validate the ai4-metadata.yml before making a git push using:

pip install yq
pip install ai4-metadata
yq -e -j ai4-metadata.yml >ai4-metadata.json
ai4-metadata validate ai4-metadata.json