{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/thanosnet-a-novel-trash-classification-method","title":"ThanosNet: A Novel Trash Classification Method Using Metadata","arxiv_id":null,"date":"2021-03-19","proceeding":"IEEE International Conference on Big Data 2021 3","authors":["Alan Sun","Harry Xiao"],"abstract":"Recent progress in deep neural networks has spurred significant development of image-based trash classification literature. These methods predominately use transfer learning to achieve state-of-the-art results. In this contribution, a new methodology is introduced that uses metadata fields such as location and time-based traffic intensity to assist existing image-based classifiers. We curated ISBNet, a dataset which contains 889 images and their associated metadata, distributed over 5 classes (paper, plastic, cans, tetra pak, and landfill). This dataset was used to develop our model, ThanosNet, which is superior to current state-of-the-art, image-based, trash classification models. Although ISBNet is localized to one user community, the general methodology developed here is applicable to a wide array of consumer contexts.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9378287","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9378287","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"thanosnet-a-novel-trash-classification-method","repo_url":"https://github.com/alansun17904/smart-trash","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"isbnet","name":"ISBNet","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-classification-on-isbnet","task":"Image Classification","dataset":"ISBNet","model":"ThanosNet","rank_in_archive_order":1,"of":1,"metrics":{"Macro F1":"0.952"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}