{"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":"/code/bbox-transform-inv","entry":"bbox_transform_inv","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":11,"n_papers_ran":5,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":9,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":13,"n_places_pointer_only":0,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":7},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"1911.09070","paper":"/paper/efficientdet-scalable-and-efficient-object","title":"EfficientDet: Scalable and Efficient Object Detection","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuannianz/EfficientDet","path":"layers.py","file_url":"https://github.com/xuannianz/EfficientDet/blob/HEAD/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8a24d71a96af0391","mcp_get_code":{"code_sha256":"8a24d71a96af0391"}},{"arxiv_id":"1911.08299","paper":"/paper/learning-modulated-loss-for-rotated-object","title":"Learning Modulated Loss for Rotated Object Detection","date":"2019-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mrqianduoduo/RSDet-8P-4R","path":"libs/box_utils/bbox_transform.py","file_url":"https://github.com/Mrqianduoduo/RSDet-8P-4R/blob/HEAD/libs/box_utils/bbox_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"41aa50153cd73204","mcp_get_code":{"code_sha256":"41aa50153cd73204"}},{"arxiv_id":"1906.02283","paper":"/paper/improving-retinanet-for-ct-lesion-detection","title":"Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels","date":"2019-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fizyr/keras-retinanet","path":"keras_retinanet/backend/backend.py","file_url":"https://github.com/fizyr/keras-retinanet/blob/HEAD/keras_retinanet/backend/backend.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5b0e0c7a212d74ca","mcp_get_code":{"code_sha256":"5b0e0c7a212d74ca"}},{"arxiv_id":"1807.03342","paper":"/paper/pcl-proposal-cluster-learning-for-weakly","title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ppengtang/oicr","path":"lib/fast_rcnn/bbox_transform.py","file_url":"https://github.com/ppengtang/oicr/blob/HEAD/lib/fast_rcnn/bbox_transform.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3695ebe0c382b05","mcp_get_code":{"code_sha256":"b3695ebe0c382b05"}},{"arxiv_id":"1803.09845","paper":"/paper/neural-baby-talk","title":"Neural Baby Talk","date":"2018-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiasenlu/NeuralBabyTalk","path":"misc/bbox_transform.py","file_url":"https://github.com/jiasenlu/NeuralBabyTalk/blob/HEAD/misc/bbox_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"826e6d34ae25c870","mcp_get_code":{"code_sha256":"826e6d34ae25c870"}},{"arxiv_id":"1702.06355","paper":"/paper/object-detection-in-videos-with-tubelet","title":"Object Detection in Videos with Tubelet Proposal Networks","date":"2017-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"myfavouritekk/tpn","path":"src/tpn/bidirectional_recurrent_extract_features.py","file_url":"https://github.com/myfavouritekk/tpn/blob/HEAD/src/tpn/bidirectional_recurrent_extract_features.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3695ebe0c382b05","mcp_get_code":{"code_sha256":"b3695ebe0c382b05"}},{"arxiv_id":"1612.03144","paper":"/paper/feature-pyramid-networks-for-object-detection","title":"Feature Pyramid Networks for Object Detection","date":"2016-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yfnn/fused-model","path":"lib/model/bbox_transform.py","file_url":"https://github.com/yfnn/fused-model/blob/HEAD/lib/model/bbox_transform.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3695ebe0c382b05","mcp_get_code":{"code_sha256":"b3695ebe0c382b05"}},{"arxiv_id":"1611.06607","paper":"/paper/a-hierarchical-approach-for-generating","title":"A Hierarchical Approach for Generating Descriptive Image Paragraphs","date":"2016-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"InnerPeace-Wu/im2p-tensorflow","path":"lib/fast_rcnn/bbox_transform.py","file_url":"https://github.com/InnerPeace-Wu/im2p-tensorflow/blob/HEAD/lib/fast_rcnn/bbox_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b2ace662cea2a36","mcp_get_code":{"code_sha256":"6b2ace662cea2a36"}},{"arxiv_id":"1609.03605","paper":"/paper/detecting-text-in-natural-image-with","title":"Detecting Text in Natural Image with Connectionist Text Proposal Network","date":"2016-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sanster/tf_ctpn","path":"lib/model/bbox_transform.py","file_url":"https://github.com/Sanster/tf_ctpn/blob/HEAD/lib/model/bbox_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db910de1a7c014bc","mcp_get_code":{"code_sha256":"db910de1a7c014bc"}},{"arxiv_id":"1609.03605","paper":"/paper/detecting-text-in-natural-image-with","title":"Detecting Text in Natural Image with Connectionist Text Proposal Network","date":"2016-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alorozco53/text-detection-ctpn","path":"lib/fast_rcnn/bbox_transform.py","file_url":"https://github.com/alorozco53/text-detection-ctpn/blob/HEAD/lib/fast_rcnn/bbox_transform.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d49676163bce1480","mcp_get_code":{"code_sha256":"d49676163bce1480"}},{"arxiv_id":"1506.02025","paper":"/paper/spatial-transformer-networks","title":"Spatial Transformer Networks","date":"2015-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenwuperth/rgz_rcnn","path":"lib/fast_rcnn/bbox_transform.py","file_url":"https://github.com/chenwuperth/rgz_rcnn/blob/HEAD/lib/fast_rcnn/bbox_transform.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3695ebe0c382b05","mcp_get_code":{"code_sha256":"b3695ebe0c382b05"}},{"arxiv_id":"1506.01497","paper":"/paper/faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanxp/rcnn-discovery","path":"lib/layer_utils/proposal_layer.py","file_url":"https://github.com/yanxp/rcnn-discovery/blob/HEAD/lib/layer_utils/proposal_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ed64f0c42ae635d5","mcp_get_code":{"code_sha256":"ed64f0c42ae635d5"}},{"arxiv_id":"1506.01497","paper":"/paper/faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ithuanhuan/gpu-py3-faster-rcnn","path":"lib/layer_utils/proposal_layer.py","file_url":"https://github.com/ithuanhuan/gpu-py3-faster-rcnn/blob/HEAD/lib/layer_utils/proposal_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3695ebe0c382b05","mcp_get_code":{"code_sha256":"b3695ebe0c382b05"}}]}