Code for Sacramento; Moore Institute for Plastic Pollution Research; CALTRANS


[Copied from website on 10-17-2023. Check page for updated information.]
Manuscript -> Cowger et al. (2023). “Trash AI: A Web GUI for Serverless Computer Vision Analysis of Images of Trash.” The Journal of Open Source Software.
 TrashAI is an open source project developed and maintained by Code For Sacramento in partnership with Win Cowger from The Moore Institute for Plastic Pollution Research and Walter Yu from CALTRANSSteven Hollingsworth is the lead developer and contributor to the code base.


To get started, visit the Upload Tab or click here.


What is it?

Trash AI allows you to upload an image containing trash and get back data about the trash in the image, including the classification of trash and bounding box of where the trash is in the image.

How does it work?

Trash AI builds a model using the YoloV5 toolset trained on the TACO dataset. The model takes an image containing trash and returns a list of annotations and bounding boxes of trash within the image. The model is imported into the front-end Vue.js application where it is invoked when an image is uploaded. The Vue application then displays the results of the model on the image.

How can I use Trash AI?

Trash AI is open source and free to use however you see fit. You may classify images and download the data. You may copy and modify the code for your own use.

Disclaimer about uploaded images

The current version of Trash AI and the model we are using is just a start! The tool works best for images of individual pieces of trash imaged less than 1 meter away from the camera. We are looking for collaborators who can help us improve this project.

Reporting issues and improvements

If you would like to report an issue or request a feature, please open a Github Issue in our repository. If you would like to provide more general feedback, please fill out our feedback form here .