{"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/nas-fpn-learning-scalable-feature-pyramid","title":"NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection","arxiv_id":"1904.07392","date":"2019-04-16","proceeding":"CVPR 2019 6","authors":["Golnaz Ghiasi","Tsung-Yi Lin","Ruoming Pang","Quoc V. Le"],"abstract":"Current state-of-the-art convolutional architectures for object detection are\nmanually designed. Here we aim to learn a better architecture of feature\npyramid network for object detection. We adopt Neural Architecture Search and\ndiscover a new feature pyramid architecture in a novel scalable search space\ncovering all cross-scale connections. The discovered architecture, named\nNAS-FPN, consists of a combination of top-down and bottom-up connections to\nfuse features across scales. NAS-FPN, combined with various backbone models in\nthe RetinaNet framework, achieves better accuracy and latency tradeoff compared\nto state-of-the-art object detection models. NAS-FPN improves mobile detection\naccuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in\n[32] and achieves 48.3 AP which surpasses Mask R-CNN [10] detection accuracy\nwith less computation time.","url_abs":"http://arxiv.org/abs/1904.07392v1","url_pdf":"http://arxiv.org/pdf/1904.07392v1.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":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/tensorflow/tpu/tree/master/models/official/detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/nas-fpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/MS-Mind/MS-Code-08/tree/main/nas-fpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/Mind23-2/MindCode-101/tree/main/nasnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/Mind23-2/MindCode-3/tree/main/nas-fpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/code-implementation1/Code6/tree/main/nas-fpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/mindspore-ai/models/tree/master/research/cv/nas-fpn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"nas-fpn-learning-scalable-feature-pyramid","repo_url":"https://github.com/open-mmlab/mmdetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"architecture-search","task_name":"Neural Architecture Search"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"amoebanet","method_name":"AmoebaNet"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dropblock","method_name":"DropBlock"},{"method_slug":"focal-loss","method_name":"Focal Loss"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"nas-fpn","method_name":"NAS-FPN"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"retinanet","method_name":"RetinaNet"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"spatially-separable-convolution","method_name":"Spatially Separable Convolution"},{"method_slug":"step-decay","method_name":"Step Decay"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[{"slug":"nas-fpn","name":"NAS-FPN","full_name":"NAS-FPN"}],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.07392","atlas_url":"https://app.syntology.ai/?focus=1904.07392","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}