{"url":"/method/retinanet-rs","slug":"retinanet-rs","name":"RetinaNet-RS","full_name":"RetinaNet-RS","full_name_withheld":false,"description_markdown":"**RetinaNet-RS** is an object detection model produced through a model scaling method based on changing the the input resolution and [ResNet](https://paperswithcode.com/method/resnet) backbone depth. For [RetinaNet](https://paperswithcode.com/method/retinanet), we scale up input resolution from 512 to 768 and the ResNet backbone depth from 50 to 152. As RetinaNet performs dense one-stage object detection, the authors find scaling up input resolution leads to large resolution feature maps hence more anchors to process. This results in a higher capacity dense prediction heads and expensive NMS. Scaling stops at input resolution 768 × 768 for RetinaNet.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2107.00057v1","title":"Simple Training Strategies and Model Scaling for Object Detection","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"One-Stage Object Detection Models","url":"/methods/category/one-stage-object-detection-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"Optimizing Anchor-based Detectors for Autonomous Driving Scenes","date":"2022-08-11","arxiv_id":"2208.06062","n_code_links":0,"syntology":null},{"paper":"/paper/simple-training-strategies-and-model-scaling","title":"Simple Training Strategies and Model Scaling for Object Detection","date":"2021-06-30","arxiv_id":"2107.00057","n_code_links":1,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":1},{"task":null,"name":"GPU","papers":1},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-detection","name":"Object Detection","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/object-detection-1","name":"object-detection","papers":1}],"tasks_shown":7,"n_tasks":7,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":1}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/retinanet-rs"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}