{"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/on-generalizing-detection-models-for","title":"On Generalizing Detection Models for Unconstrained Environments","arxiv_id":"1909.13080","date":"2019-09-28","proceeding":null,"authors":["Prajjwal Bhargava"],"abstract":"Object detection has seen tremendous progress in recent years. However, current algorithms don't generalize well when tested on diverse data distributions. We address the problem of incremental learning in object detection on the India Driving Dataset (IDD). Our approach involves using multiple domain-specific classifiers and effective transfer learning techniques focussed on avoiding catastrophic forgetting. We evaluate our approach on the IDD and BDD100K dataset. Results show the effectiveness of our domain adaptive approach in the case of domain shifts in environments.","url_abs":"https://arxiv.org/abs/1909.13080v1","url_pdf":"https://arxiv.org/pdf/1909.13080v1.pdf","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":"on-generalizing-detection-models-for","repo_url":"https://github.com/prajjwal1/autonomous-object-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-bdd100k-val","task":"Object Detection","dataset":"BDD100K val","model":"hybrid incremental net","rank_in_archive_order":1,"of":1,"metrics":{"mAP@0.5":"45.7"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-india-driving-dataset","task":"Object Detection","dataset":"India Driving Dataset","model":"hybrid incremental net","rank_in_archive_order":2,"of":4,"metrics":{"mAP@0.5":"31.57"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}