[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-184265-en":3,"doc-seo-184265-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},184265,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",6,"Technology","1506.02640v5","Object detection performance results are presented across multiple methods and datasets, using metrics such as mAP and AP. Comparisons include YOLO, Fast YOLO, Fast R-CNN variants, Faster R-CNN, MR CNN variants, HyperNet variants, DEEP ENS COCO, and other approaches. The tables report numeric scores for class-wise categories on VOC-style evaluations and summarize outcomes on VOC 2007 AP alongside additional benchmarks like People-Art AP, enabling method ranking and trade-off analysis.","| 100Hz DPM [31] | 2007 | 16.0 | 100 |\n| --- | --- | --- | --- |\n| 30Hz DPM [31] | 2007 | 26.1 | 30 |\n| Fast YOLO | 2007+2012 | 52.7 | 155 |\n| YOLO | 2007+2012 | 63.4 | 45 |\n| Less Than Real-Time |  |  |  |\n\n| VOC 2012 test | mAP | aero bike bird boat bottle bus car cat chair cow table dog horse mbike personplant sheep sofa train tv |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| MR CNN MORE DATA [11] | 73.9 | 85.5 | 82.9 | 76.6 | 57.8 | 62.7 | 79.4 | 77.2 | 86.6 | 55.0 | 79.1 | 62.2 | 87.0 | 83.4 | 84.7 | 78.9 | 45.3 | 73.4 | 65.8 | 80.3 | 74.0 |\n| HyperNet VGG | 71.4 | 84.2 | 78.5 | 73.6 | 55.6 | 53.7 | 78.7 | 79.8 | 87.7 | 49.6 | 74.9 | 52.1 | 86.0 | 81.7 | 83.3 | 81.8 | 48.6 | 73.5 | 59.4 | 79.9 | 65.7 |\n| HyperNet  SP | 71.3 | 84.1 | 78.3 | 73.3 | 55.5 | 53.6 | 78.6 | 79.6 | 87.5 | 49.5 | 74.9 | 52.1 | 85.6 | 81.6 | 83.2 | 81.6 | 48.4 | 73.2 | 59.3 | 79.7 | 65.6 |\n| Fast R-CNN + YOLO | 70.7 | 83.4 | 78.5 | 73.5 | 55.8 | 43.4 | 79.1 | 73.1 | 89.4 | 49.4 | 75.5 | 57.0 | 87.5 | 80.9 | 81.0 | 74.7 | 41.8 | 71.5 | 68.5 | 82.1 | 67.2 |\n| MR CNN S CNN [11] | 70.7 | 85.0 | 79.6 | 71.5 | 55.3 | 57.7 | 76.0 | 73.9 | 84.6 | 50.5 | 74.3 | 61.7 | 85.5 | 79.9 | 81.7 | 76.4 | 41.0 | 69.0 | 61.2 | 77.7 | 72.1 |\n| Faster R-CNN [28] | 70.4 | 84.9 | 79.8 | 74.3 | 53.9 | 49.8 | 77.5 | 75.9 | 88.5 | 45.6 | 77.1 | 55.3 | 86.9 | 81.7 | 80.9 | 79.6 | 40.1 | 72.6 | 60.9 | 81.2 | 61.5 |\n| DEEP ENS  COCO | 70.1 | 84.0 | 79.4 | 71.6 | 51.9 | 51.1 | 74.1 | 72.1 | 88.6 | 48.3 | 73.4 | 57.8 | 86.1 | 80.0 | 80.7 | 70.4 | 46.6 | 69.6 | 68.8 | 75.9 | 71.4 |\n| NoC [29] | 68.8 | 82.8 | 79.0 | 71.6 | 52.3 | 53.7 | 74.1 | 69.0 | 84.9 | 46.9 | 74.3 | 53.1 | 85.0 | 81.3 | 79.5 | 72.2 | 38.9 | 72.4 | 59.5 | 76.7 | 68.1 |\n| Fast R-CNN [14] | 68.4 | 82.3 | 78.4 | 70.8 | 52.3 | 38.7 | 77.8 | 71.6 | 89.3 | 44.2 | 73.0 | 55.0 | 87.5 | 80.5 | 80.8 | 72.0 | 35.1 | 68.3 | 65.7 | 80.4 | 64.2 |\n| UMICH FGS  STRUCT | 66.4 | 82.9 | 76.1 | 64.1 | 44.6 | 49.4 | 70.3 | 71.2 | 84.6 | 42.7 | 68.6 | 55.8 | 82.7 | 77.1 | 79.9 | 68.7 | 41.4 | 69.0 | 60.0 | 72.0 | 66.2 |\n| NUS NIN C2000 [7] | 63.8 | 80.2 | 73.8 | 61.9 | 43.7 | 43.0 | 70.3 | 67.6 | 80.7 | 41.9 | 69.7 | 51.7 | 78.2 | 75.2 | 76.9 | 65.1 | 38.6 | 68.3 | 58.0 | 68.7 | 63.3 |\n| BabyLearning [7] | 63.2 | 78.0 | 74.2 | 61.3 | 45.7 | 42.7 | 68.2 | 66.8 | 80.2 | 40.6 | 70.0 | 49.8 | 79.0 | 74.5 | 77.9 | 64.0 | 35.3 | 67.9 | 55.7 | 68.7 | 62.6 |\n| NUS NIN | 62.4 | 77.9 | 73.1 | 62.6 | 39.5 | 43.3 | 69.1 | 66.4 | 78.9 | 39.1 | 68.1 | 50.0 | 77.2 | 71.3 | 76.1 | 64.7 | 38.4 | 66.9 | 56.2 | 66.9 | 62.7 |\n| R-CNN VGG BB [13] | 62.4 | 79.6 | 72.7 | 61.9 | 41.2 | 41.9 | 65.9 | 66.4 | 84.6 | 38.5 | 67.2 | 46.7 | 82.0 | 74.8 | 76.0 | 65.2 | 35.6 | 65.4 | 54.2 | 67.4 | 60.3 |\n| R-CNN VGG [13] | 59.2 | 76.8 | 70.9 | 56.6 | 37.5 | 36.9 | 62.9 | 63.6 | 81.1 | 35.7 | 64.3 | 43.9 | 80.4 | 71.6 | 74.0 | 60.0 | 30.8 | 63.4 | 52.0 | 63.5 | 58.7 |\n| YOLO | 57.9 | 77.0 | 67.2 | 57.7 | 38.3 | 22.7 | 68.3 | 55.9 | 81.4 | 36.2 | 60.8 | 48.5 | 77.2 | 72.3 | 71.3 | 63.5 | 28.9 | 52.2 | 54.8 | 73.9 | 50.8 |\n| Feature Edit [33] | 56.3 | 74.6 | 69.1 | 54.4 | 39.1 | 33.1 | 65.2 | 62.7 | 69.7 | 30.8 | 56.0 | 44.6 | 70.0 | 64.4 | 71.1 | 60.2 | 33.3 | 61.3 | 46.4 | 61.7 | 57.8 |\n| R-CNN BB [13] | 53.3 | 71.8 | 65.8 | 52.0 | 34.1 | 32.6 | 59.6 | 60.0 | 69.8 | 27.6 | 52.0 | 41.7 | 69.6 | 61.3 | 68.3 | 57.8 | 29.6 | 57.8 | 40.9 | 59.3 | 54.1 |\n| SDS [16] | 50.7 | 69.7 | 58.4 | 48.5 | 28.3 | 28.8 | 61.3 | 57.5 | 70.8 | 24.1 | 50.7 | 35.9 | 64.9 | 59.1 | 65.8 | 57.1 | 26.0 | 58.8 | 38.6 | 58.9 | 50.7 |\n| R-CNN [13] | 49.6 | 68.1 | 63.8 | 46.1 | 29.4 | 27.9 | 56.6 | 57.0 | 65.9 | 26.5 | 48.7 | 39.5 | 66.2 | 57.3 | 65.4 | 53.2 | 26.2 | 54.5 | 38.1 | 50.6 | 51.6 |\n\n|  | VOC 2007 AP | Picasso |  | People-Art AP |\n| --- | --- | --- | --- | --- |\n|  |  | AP | Best F1 |  |\n| YOLO | 59.2 | 53.3 | 0.590 | 45 |\n| R-CNN | 54.2 |","cbCaigJsTQTd7Epn","https://ap.wps.com/l/cbCaigJsTQTd7Epn","pdf",5296750,1,10,"English","en",105,"# Results comparison\n## VOC 2012 mAP\n## VOC 2007 AP and additional benchmarks","[{\"question\":\"Which evaluation metrics are used in the tables?\",\"answer\":\"The document reports mAP for VOC 2012 test and AP (including VOC 2007 AP) for additional comparisons, sometimes paired with best F1 and People-Art AP.\"},{\"question\":\"What detection methods are compared?\",\"answer\":\"Methods include YOLO and Fast YOLO, R-CNN and Fast R-CNN variants, Faster R-CNN, MR CNN/S CNN, HyperNet variants, DEEP ENS COCO, and other named approaches shown in the tables.\"},{\"question\":\"How are the results organized by dataset or benchmark?\",\"answer\":\"Results are grouped under VOC 2012 test class-wise mAP tables and a separate section listing VOC 2007 AP with additional benchmark columns such as Picasso and People-Art AP.\"}]","1506.02640v5 | 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