{"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/a-large-contextual-dataset-for-classification","title":"A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning","arxiv_id":"1609.04453","date":"2016-09-14","proceeding":null,"authors":["T. Nathan Mundhenk","Goran Konjevod","Wesam A. Sakla","Kofi Boakye"],"abstract":"We have created a large diverse set of cars from overhead images, which are\nuseful for training a deep learner to binary classify, detect and count them.\nThe dataset and all related material will be made publically available. The set\ncontains contextual matter to aid in identification of difficult targets. We\ndemonstrate classification and detection on this dataset using a neural network\nwe call ResCeption. This network combines residual learning with\nInception-style layers and is used to count cars in one look. This is a new way\nto count objects rather than by localization or density estimation. It is\nfairly accurate, fast and easy to implement. Additionally, the counting method\nis not car or scene specific. It would be easy to train this method to count\nother kinds of objects and counting over new scenes requires no extra set up or\nassumptions about object locations.","url_abs":"http://arxiv.org/abs/1609.04453v1","url_pdf":"http://arxiv.org/pdf/1609.04453v1.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":[],"tasks":[{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"cowc","name":"COWC","full_name":"Cars Overhead With Context"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-counting-on-carpk","task":"Object Counting","dataset":"CARPK","model":"One-Look Regression (2016)","rank_in_archive_order":10,"of":15,"metrics":{"MAE":"21.88","RMSE":"36.73"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.04453","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}