This project provides a deep learning image auto-tagging classifier as a JSON REST API. It is intended to be used as part of this project.
It currently uses Caffe2 and the squeezenet pre-trained model. In time I intend to transfer and train from a dataset that has more relevant categories and better accuracy. This could even be user contributed eventually.
git clone git@github.com:damianmoore/photo-manager.git
cd photo-manager-classifier/Edit the file docker-compose.yml to mount your directory of photos as the volume /photos then build and run.
docker-compose build
docker-compose upIt will take a while to build the Docker image as it is based off a large deep-learning image that is over 900MB to download. This can be reduced in future as we don't need all the included resources and trained models. It works for now as a means to get up and running.
Once the server is running you should be able to use curl or HTTPie to issue requests, passing a path to a file in the mounted volume directory.
curl:
curl -H "Content-Type: application/json" -X GET -d '{"path": "/photos/IMG_6085.jpg"}' http://localhost:8888/categories/HTTPie:
http GET localhost:8888/categories/ path='/photos/IMG_6085.jpg'You should get a response (after a few seconds) like this:
{
"categories": [
[
"acoustic guitar",
0.4404420852661133
],
[
"rifle",
0.07927235215902328
],
[
"bannister, banister, balustrade, balusters, handrail",
0.05965603142976761
],
],
"path": "/photos/IMG_6085.jpg",
"version": 0
}