{"work":{"id":"141b45d1-5684-47b9-8fd4-e8171a73b18d","openalex_id":null,"doi":null,"arxiv_id":"1504.08083","raw_key":null,"title":"Fast R-CNN","authors":null,"authors_text":"Girshick, Ross","year":2015,"venue":"cs.CV","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.","external_url":"https://arxiv.org/abs/1504.08083","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-25T18:51:08.839860+00:00","pith_arxiv_id":"1504.08083","created_at":"2026-05-11T13:16:21.416439+00:00","updated_at":"2026-05-25T18:51:08.839860+00:00","title_quality_ok":false,"display_title":"Fast r-cnn","render_title":"Fast r-cnn"},"hub":{"state":{"work_id":"141b45d1-5684-47b9-8fd4-e8171a73b18d","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":11,"external_cited_by_count":null,"distinct_field_count":4,"first_pith_cited_at":"2015-10-01T09:03:44+00:00","last_pith_cited_at":"2026-05-20T03:09:45+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-06-03T06:05:50.983977+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":1},{"context_role":"method","n":1}],"polarity_counts":[{"context_polarity":"background","n":1},{"context_polarity":"use_method","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}