{"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/evaluating-resnext-model-architecture-for","title":"Evaluating ResNeXt Model Architecture for Image Classification","arxiv_id":"1805.08700","date":"2018-05-09","proceeding":null,"authors":["Saifuddin Hitawala"],"abstract":"In recent years, deep learning methods have been successfully applied to\nimage classification tasks. Many such deep neural networks exist today that can\neasily differentiate cats from dogs. One such model is the ResNeXt model that\nuses a homogeneous, multi-branch architecture for image classification. This\npaper aims at implementing and evaluating the ResNeXt model architecture on\nsubsets of the CIFAR-10 dataset. It also tweaks the original ResNeXt\nhyper-parameters such as cardinality, depth and base-width and compares the\nperformance of the modified model with the original. Analysis of the\nexperiments performed in this paper show that a slight decrease in depth or\nbase-width does not affect the performance of the model much leading to\ncomparable results.","url_abs":"http://arxiv.org/abs/1805.08700v1","url_pdf":"http://arxiv.org/pdf/1805.08700v1.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":"evaluating-resnext-model-architecture-for","repo_url":"https://github.com/saifhitawala/ResNeXt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"resnext","method_name":"ResNeXt"},{"method_slug":"resnext-block","method_name":"ResNeXt Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}