{"doi":"10.1109/iccv.2015.169","title":"Fast R-CNN","abstract":"This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.","journal":"2015 IEEE International Conference on Computer Vision (ICCV)","year":2015,"id":218,"datarank":20.567994463887473,"base_score":10.225063676635465,"endowment":10.225063676635465,"self_citation_contribution":1.53375955149532,"citation_network_contribution":19.034234912392154,"self_endowment_contribution":1.53375955149532,"citer_contribution":19.034234912392154,"corpus_percentile":98.8,"corpus_rank":174,"citation_count":27585,"citer_count":187,"citers_with_citation_signal":187,"citers_with_endowment":187,"datacite_reuse_total":0,"is_dataset":false,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2015-12-01","authors":[{"id":2013,"name":"Ross Girshick","orcid":null,"position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}