- Sponsor: Win Cowger, Moore Institute for Plastic Pollution Research
- Meeting Times: Wednesdays at 6:30pm PT Weekly Brigade Meetings
- Publication Pending: paper.md
Trash AI is a web application where users can upload photos of litter, which will be labeled using computer vision to detect and categorize litter in the image by type. Early inspiration from WADE AI streamlined this development. Trash AI will enhance the abilities of researchers to quickly label trash in photos.
You can simply go to www.trashai.org to start using the tool or deploy it yourself. Current self-deployment options are local deployment with docker to remote on Amazon Web Services (AWS).
docker run -p 5150:5150 -it code4sac/trashai:latest
Navigate to http://localhost:5150
If you are attempting to run on an Apple Silicon device, you might get the following error:
docker: no matching manifest for linux/arm64/v8 in the manifest list entries.
This can be remedied by specifying the build platform.
docker run --platform linux/x86_64 -p 5150:5150 -it code4sac/trashai:latest
If you want to deploy this to a static web directory and serve it using
apache or nginx, you can do so with the following command using /var/www/html
as an example destination directory.
# create container from latest public trash ai docker image
id=$(docker create code4sac/trashai:latest)
# copy the static files
docker cp $id:/usr/share/nginx/html /var/www/html
# remove created container
docker rm -v $id
- Run the environment live with localstack and docker.
- Instructions on bringing up a new AWS deployment.
- Mostly CD at this point.
- Runs the complex stuff so you don't have to.
Instructions for automated and manual tests here.
Documentation on typical workflow and functionality of the tool can be found here
We welcome contributions of all kinds.
To get started, look at the Start Here section of the project board
You can open an issue or pull request.
Here are some ideas on How to Contribute.
Please adhere to this project's Code of Conduct.