{"id":4111,"date":"2020-03-09T09:42:46","date_gmt":"2020-03-09T01:42:46","guid":{"rendered":"http:\/\/pairlabs.ai.pro6.designworks.tw\/?p=4111"},"modified":"2021-05-11T11:45:32","modified_gmt":"2021-05-11T03:45:32","slug":"icme2020-grand-challenge-pair-%e7%ab%b6%e8%b3%bd-%e5%9c%93%e6%bb%bf%e8%90%bd%e5%b9%95","status":"publish","type":"post","link":"https:\/\/pairlabs.ai\/en\/icme2020-grand-challenge-pair-%e7%ab%b6%e8%b3%bd-%e5%9c%93%e6%bb%bf%e8%90%bd%e5%b9%95\/","title":{"rendered":"ICME2020 Grand Challenge: The PAIR competition ended successfully"},"content":{"rendered":"<p><span style=\"font-size: 12pt; font-family: 'Noto Sans TC';\">2020-03-09<\/span><\/p>\n<p>&nbsp;<\/p>\n<p>The &#8220;ICME2020 Grand Challenge-Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries&#8221; co-sponsored by PAIR Labs and <a href=\"http:\/\/ivs.ee.nctu.edu.tw\/ivs\/index.php\/advisor\">iVS Lab (Directed by Prof. Jiun-In Guo)<\/a> has successfully concluded on March 6. This competition is mainly aimed at the unique transportation and road conditions in Asia. After two months of qualification competition and four weeks of final competition, the winners and special awards were finally released.(Photo source: <a href=\"https:\/\/pixabay.com\/zh\/photos\/taxi-cab-traffic-cab-new-york-381233\/\">Pixabay<\/a>)<\/p>\n<p>&nbsp;<\/p>\n<h3>Award Winners<\/h3>\n<ul>\n<li>Champion: USTC-NELSLIP<\/li>\n<li>First Runner-up: BUPT_MCPRL<\/li>\n<li>Second Runner-up: DD_VISION<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Special Awards<\/h3>\n<ul>\n<li>Best accuracy award: BUPT_MCPRL<\/li>\n<li>Best bicycle detection award: IBDO-AIOT<\/li>\n<li>Best scooter detection award: Deep Learner<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Final Evaluation Result<\/h3>\n<p>&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td rowspan=\"2\" width=\"85\"><strong>group<\/strong><\/td>\n<td rowspan=\"2\" width=\"76\"><strong>Name<\/strong><\/td>\n<td colspan=\"5\" width=\"151\"><strong>Accuracy %<\/strong><\/td>\n<td rowspan=\"2\" width=\"76\"><strong>Model Size<\/strong><\/p>\n<p><strong>(MByte)<\/strong><\/td>\n<td rowspan=\"2\" width=\"83\"><strong>Complexity<\/strong><\/p>\n<p><strong>(GOPS\/frame)<\/strong><\/td>\n<td rowspan=\"2\" width=\"83\"><strong>Speed<\/strong><\/p>\n<p><strong>(ms\/frame)<\/strong><\/td>\n<\/tr>\n<tr>\n<td colspan=\"2\" width=\"50\"><strong>mAP<\/strong><\/td>\n<td width=\"50\"><strong>bicycle<\/strong><\/td>\n<td colspan=\"2\" width=\"50\"><strong>scooter<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_01<\/strong><\/td>\n<td width=\"76\">BUPT_MCPRL<\/td>\n<td colspan=\"2\" width=\"50\">49.20<\/td>\n<td width=\"50\">0.90<\/td>\n<td colspan=\"2\" width=\"50\">59.50<\/td>\n<td width=\"76\">7.35<\/td>\n<td width=\"83\">12.89<\/td>\n<td width=\"83\">401.21<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_02<\/strong><\/td>\n<td width=\"76\">USTC-NELSLIP<\/td>\n<td colspan=\"2\" width=\"50\">44.60<\/td>\n<td width=\"50\">0.20<\/td>\n<td colspan=\"2\" width=\"50\">56.00<\/td>\n<td width=\"76\">6.04<\/td>\n<td width=\"83\">11.16<\/td>\n<td width=\"83\">141.8<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_03<\/strong><\/td>\n<td width=\"76\">DD_VISION<\/td>\n<td colspan=\"2\" width=\"50\">25.60<\/td>\n<td width=\"50\">2.00<\/td>\n<td colspan=\"2\" width=\"50\">29.40<\/td>\n<td width=\"76\">0.86<\/td>\n<td width=\"83\">0.52<\/td>\n<td width=\"83\">56.18<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_04<\/strong><\/td>\n<td width=\"76\">Deep Learner<\/td>\n<td colspan=\"2\" width=\"50\">49.00<\/td>\n<td width=\"50\">0.10<\/td>\n<td colspan=\"2\" width=\"50\">62.40<\/td>\n<td width=\"76\">45.2<\/td>\n<td width=\"83\">56.64<\/td>\n<td width=\"83\">1560.43<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_06<\/strong><\/td>\n<td width=\"76\">nccu_vipl<\/td>\n<td colspan=\"2\" width=\"50\">38.70<\/td>\n<td width=\"50\">0.00<\/td>\n<td colspan=\"2\" width=\"50\">48.20<\/td>\n<td width=\"76\">187.27<\/td>\n<td width=\"83\">285.7<\/td>\n<td width=\"83\">287.11<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_07<\/strong><\/td>\n<td width=\"76\">IBDO-AIOT<\/td>\n<td colspan=\"2\" width=\"50\">47.00<\/td>\n<td width=\"50\">12.00<\/td>\n<td colspan=\"2\" width=\"50\">53.70<\/td>\n<td width=\"76\">215.49<\/td>\n<td width=\"83\">99.46<\/td>\n<td width=\"83\">567.65<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_08<\/strong><\/td>\n<td width=\"76\">ACVLab<\/td>\n<td colspan=\"2\" width=\"50\">41.10<\/td>\n<td width=\"50\">0.10<\/td>\n<td colspan=\"2\" width=\"50\">49.90<\/td>\n<td width=\"76\">87.46<\/td>\n<td width=\"83\">31.82<\/td>\n<td width=\"83\">696.72<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_010<\/strong><\/td>\n<td width=\"76\">\u8cc7\u5de5A<\/td>\n<td colspan=\"2\" width=\"50\">8.80<\/td>\n<td width=\"50\">0.00<\/td>\n<td colspan=\"2\" width=\"50\">3.10<\/td>\n<td width=\"76\">298.6<\/td>\n<td width=\"83\">140.35<\/td>\n<td width=\"83\">653.71<\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_05<\/strong><\/td>\n<td width=\"76\">Rock4Ever<\/td>\n<td width=\"47\"><\/td>\n<td colspan=\"3\" width=\"57\"><\/td>\n<td width=\"47\"><\/td>\n<td width=\"76\"><\/td>\n<td width=\"83\"><\/td>\n<td width=\"83\"><\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><strong>icme2020_09<\/strong><\/td>\n<td width=\"76\">jummy112<\/td>\n<td width=\"47\"><\/td>\n<td colspan=\"3\" width=\"57\"><\/td>\n<td width=\"47\"><\/td>\n<td width=\"76\"><\/td>\n<td width=\"83\"><\/td>\n<td width=\"83\"><\/td>\n<\/tr>\n<tr>\n<td width=\"85\"><\/td>\n<td width=\"76\"><\/td>\n<td width=\"47\"><\/td>\n<td width=\"3\"><\/td>\n<td width=\"50\"><\/td>\n<td width=\"3\"><\/td>\n<td width=\"47\"><\/td>\n<td width=\"76\"><\/td>\n<td width=\"83\"><\/td>\n<td width=\"83\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3><\/h3>\n<h3>Final Score<\/h3>\n<table>\n<tbody>\n<tr>\n<td rowspan=\"2\" width=\"107\"><strong>group<\/strong><\/td>\n<td rowspan=\"2\" width=\"101\"><strong>Name<\/strong><\/td>\n<td colspan=\"4\" width=\"236\"><strong>Partial Score<\/strong><\/td>\n<td rowspan=\"2\" width=\"90\"><strong>Final Score<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"50\"><strong>mAP<\/strong><\/td>\n<td width=\"64\"><strong>model size<\/strong><\/td>\n<td width=\"75\"><strong>complexity<\/strong><\/td>\n<td width=\"48\"><strong>speed<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_01<\/strong><\/td>\n<td width=\"101\">BUPT_MCPRL<\/td>\n<td width=\"50\">25<\/td>\n<td width=\"64\">24.46<\/td>\n<td width=\"75\">23.92<\/td>\n<td width=\"48\">19.27<\/td>\n<td width=\"90\">92.64<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_02<\/strong><\/td>\n<td width=\"101\">USTC-NELSLIP<\/td>\n<td width=\"50\">22.15<\/td>\n<td width=\"64\">24.57<\/td>\n<td width=\"75\">24.07<\/td>\n<td width=\"48\">23.58<\/td>\n<td width=\"90\">94.36<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_03<\/strong><\/td>\n<td width=\"101\">DD_VISION<\/td>\n<td width=\"50\">10.4<\/td>\n<td width=\"64\">25<\/td>\n<td width=\"75\">25<\/td>\n<td width=\"48\">25<\/td>\n<td width=\"90\">85.40<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_04<\/strong><\/td>\n<td width=\"101\">Deep Learner<\/td>\n<td width=\"50\">24.88<\/td>\n<td width=\"64\">21.28<\/td>\n<td width=\"75\">20.08<\/td>\n<td width=\"48\">0<\/td>\n<td width=\"90\">66.23<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_06<\/strong><\/td>\n<td width=\"101\">nccu_vipl<\/td>\n<td width=\"50\">18.5<\/td>\n<td width=\"64\">9.35<\/td>\n<td width=\"75\">0<\/td>\n<td width=\"48\">21.16<\/td>\n<td width=\"90\">49.01<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_07<\/strong><\/td>\n<td width=\"101\">IBDO-AIOT<\/td>\n<td width=\"50\">23.64<\/td>\n<td width=\"64\">6.98<\/td>\n<td width=\"75\">16.33<\/td>\n<td width=\"48\">16.5<\/td>\n<td width=\"90\">63.44<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_08<\/strong><\/td>\n<td width=\"101\">ACVLab<\/td>\n<td width=\"50\">19.99<\/td>\n<td width=\"64\">17.73<\/td>\n<td width=\"75\">22.26<\/td>\n<td width=\"48\">14.35<\/td>\n<td width=\"90\">74.33<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_010<\/strong><\/td>\n<td width=\"101\">\u8cc7\u5de5A<\/td>\n<td width=\"50\">0<\/td>\n<td width=\"64\">0<\/td>\n<td width=\"75\">12.74<\/td>\n<td width=\"48\">15.07<\/td>\n<td width=\"90\">27.81<\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_05<\/strong><\/td>\n<td width=\"101\">Rock4Ever<\/td>\n<td width=\"50\"><\/td>\n<td width=\"64\"><\/td>\n<td width=\"75\"><\/td>\n<td width=\"48\"><\/td>\n<td width=\"90\"><\/td>\n<\/tr>\n<tr>\n<td width=\"107\"><strong>icme2020_09<\/strong><\/td>\n<td width=\"101\">jummy112<\/td>\n<td width=\"50\"><\/td>\n<td width=\"64\"><\/td>\n<td width=\"75\"><\/td>\n<td width=\"48\"><\/td>\n<td width=\"90\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>2020-03-09 &nbsp; The &#8220;ICME2020 Grand Challenge-Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries&#8221; co-sponsored by PAIR Labs and iVS Lab (Directed by Prof. Jiun-In Guo) has successfully concluded on March 6. This competition is mainly aimed at the unique transportation and road conditions in Asia. After two months of qualification [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":4117,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[126],"tags":[147,168],"class_list":["post-4111","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category--en","tag-deep_learning-en","tag-image_identification-en"],"_links":{"self":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/4111","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/comments?post=4111"}],"version-history":[{"count":5,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/4111\/revisions"}],"predecessor-version":[{"id":5978,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/4111\/revisions\/5978"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/media\/4117"}],"wp:attachment":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/media?parent=4111"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/categories?post=4111"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/tags?post=4111"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}