{"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/receptive-field-block-net-for-accurate-and","title":"Receptive Field Block Net for Accurate and Fast Object Detection","arxiv_id":"1711.07767","date":"2017-11-21","proceeding":"ECCV 2018 9","authors":["Songtao Liu","Di Huang","Yunhong Wang"],"abstract":"Current top-performing object detectors depend on deep CNN backbones, such as\nResNet-101 and Inception, benefiting from their powerful feature\nrepresentations but suffering from high computational costs. Conversely, some\nlightweight model based detectors fulfil real time processing, while their\naccuracies are often criticized. In this paper, we explore an alternative to\nbuild a fast and accurate detector by strengthening lightweight features using\na hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs)\nin human visual systems, we propose a novel RF Block (RFB) module, which takes\nthe relationship between the size and eccentricity of RFs into account, to\nenhance the feature discriminability and robustness. We further assemble RFB to\nthe top of SSD, constructing the RFB Net detector. To evaluate its\neffectiveness, experiments are conducted on two major benchmarks and the\nresults show that RFB Net is able to reach the performance of advanced very\ndeep detectors while keeping the real-time speed. Code is available at\nhttps://github.com/ruinmessi/RFBNet.","url_abs":"http://arxiv.org/abs/1711.07767v3","url_pdf":"http://arxiv.org/pdf/1711.07767v3.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":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/ruinmessi/RFBNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/Chris10M/RFB-Text-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/GivralNguyen/-Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/GivralNguyen/Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/ZTao-z/multiflow-resnet-ssd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/dishen12/py03","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"receptive-field-block-net-for-accurate-and","repo_url":"https://github.com/lyk19940625/MyRFBNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"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":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"mobilenetv1","method_name":"MobileNetV1"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"rfb","method_name":"RFB"},{"method_slug":"rfb-net","method_name":"RFB Net"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sgd-with-momentum","method_name":"SGD with Momentum"},{"method_slug":"ssd","method_name":"SSD"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[{"slug":"rfb","name":"RFB","full_name":"Receptive Field Block"},{"slug":"rfb-net","name":"RFB Net","full_name":"RFB Net"}],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1711.07767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.07767"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dishen12/py03","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/GivralNguyen/Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ruinmessi/RFBNet","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lyk19940625/MyRFBNet","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ZTao-z/multiflow-resnet-ssd","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/GivralNguyen/-Ultra-light-Vehicle-Detection-using-Tiny-Mobilenet-SSD-real-time-on-CPU-","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Chris10M/RFB-Text-Detection","reach":null}],"summary":{"ran_draft_wrong":2,"ran_fixture":1},"by_repo_kind":{"listed":{"samples":2,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"6da07489eb983afa","entry":"add_extras","repo":"dishen12/py03","repo_kind":"listed","path":"models/RFB_Net_vgg.py","file_url":"https://github.com/dishen12/py03/blob/HEAD/models/RFB_Net_vgg.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6da07489eb983afa"}},{"code_sha256_prefix":"26b1d5df5006be4c","entry":"multibox","repo":"dishen12/py03","repo_kind":"listed","path":"models/RFB_Net_vgg.py","file_url":"https://github.com/dishen12/py03/blob/HEAD/models/RFB_Net_vgg.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"26b1d5df5006be4c"}},{"code_sha256_prefix":"6921f7ed00595a4f","entry":"vgg","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"6921f7ed00595a4f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}