SimpleImageClassifier

SimpleImageClassifier is a simple example that allows you to classify a image jpg in input. This example is based on Detectron2 that is a Facebook AI Research's. Starting from this example, you can first create an installable package via pip3 and then a docker image in which it is installed the created package. The package declares the simpleimageclassifier command as entrypoint. So once the package is installed you can use this command at command line. Also, you can run it as a module, for example:

$ cd src
$ python3 -m simpleimageclassifier --config-file simpleimageclassifier/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input ../canegatto.jpg --output canegatto_out.jpg --opts MODEL.DEVICE cpu MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl

The image created in this way can be executed in a container with the following command using an jpg file placed in it:

docker run -i -t --rm --name simpleimageclassifier-cont simpleimageclassifier simpleimageclassifier --config-file /usr/local/lib/python3.8/dist-packages/simpleimageclassifier/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input <image.jpg> --output <image_out.jpg> --opts MODEL.DEVICE cpu MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl

You can also run it directly from the container shell:

$ docker run -i -t --rm --name simpleimageclassifier-cont simpleimageclassifier bash

root@7f371ac6f420:/# simpleimageclassifier --config-file /usr/local/lib/python3.8/dist-packages/detectron2/model_zoo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml --input canegatto.jpg --output canegatto_out.jpg --opts MODEL.DEVICE cpu MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl

Note the model set by --config-file can be take from detectron2 local lib in this case.

To be able to create an image from this application you need to have Docker and Docker-Compose installed on your machine and the relative python packages, see: Docker, Docker-Compose and Docker Package for Python.

Useful Commands

Create Distribution Package

python3 setup.py sdist --formats=gztar

Create Docker Image

docker build -t simpleimageclassifier .

Save Docker Image in file

docker save simpleimageclassifier | gzip > simpleimageclassifier.tar.gz

Publish Docker Image on DockerHub

Re-tagging an existing local image:

docker tag simpleimageclassifier <hub-user>/<repo-name>[:<tag>]

Login in DockerHub(use your Docker ID):

docker login

Now you can push this repository to the registry designated by its name or tag:

docker push <hub-user>/<repo-name>:<tag>

Then logout for security:

docker logout

Authors

• Giancarlo Panichi (ORCID) - ISTI-CNR Infrascience Group

License

This project is licensed under the EUPL V.1.1 License - see the LICENSE.md file for details.

About the gCube Framework

This software is part of the gCubeFramework: an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments by favouring the realisation of reuse oriented policies.

The projects leading to this software have received funding from a series of European Union programmes see FUNDING.md