{"url":"/method/regnetx","slug":"regnetx","name":"RegNetX","full_name":"RegNetX","full_name_withheld":false,"description_markdown":"**RegNetX** is a convolutional network design space with simple, regular models with parameters: depth $d$, initial width $w\\_{0} > 0$, and slope $w\\_{a} > 0$, and generates a different block width $u\\_{j}$ for each block $j < d$. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):\r\n\r\n$$ u\\_{j} = w\\_{0} + w\\_{a}\\cdot{j} $$\r\n\r\nFor **RegNetX** we have additional restrictions: we set $b = 1$ (the bottleneck ratio), $12 \\leq d \\leq 28$, and $w\\_{m} \\geq 2$ (the width multiplier).","description_state":"present","introduced_year":null,"introduced_by":{"title":"Designing Network Design Spaces","paper":"/paper/designing-network-design-spaces","first_author":"Ilija Radosavovic","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/designing-network-design-spaces"},"source":{"url":"https://arxiv.org/abs/2003.13678v1","title":"Designing Network Design Spaces","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/facebookresearch/pycls/blob/ecfb53186b426002020f1a580c3d7d7ad723e283/pycls/models/regnet.py#L50","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Convolutional Neural Networks","url":"/methods/category/convolutional-neural-networks","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models","date":"2021-11-14","arxiv_id":"2111.07355","n_code_links":0,"syntology":null},{"paper":"/paper/designing-network-design-spaces","title":"Designing Network Design Spaces","date":"2020-03-30","arxiv_id":"2003.13678","n_code_links":26,"syntology":{"ran":11,"of":53,"unverified":42,"pointer_only":0}}],"papers_shown":2,"tasks":[{"task":"/task/ensemble-learning","name":"Ensemble Learning","papers":1},{"task":"/task/fracture-detection","name":"Fracture detection","papers":1},{"task":"/task/image-classification","name":"Image Classification","papers":1},{"task":"/task/medical-object-detection","name":"Medical Object Detection","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/out-of-distribution-generalization","name":"Out-of-Distribution Generalization","papers":1},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/regnetx"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}