{"id":4110,"date":"2020-03-09T10:22:00","date_gmt":"2020-03-09T02:22:00","guid":{"rendered":"https:\/\/pairlabs.ai\/?p=3440"},"modified":"2020-03-09T10:22:00","modified_gmt":"2020-03-09T02:22:00","slug":"icme2020-grand-challenge-pair-competition-award-announced","status":"publish","type":"post","link":"https:\/\/pairlabs.ai\/en\/icme2020-grand-challenge-pair-competition-award-announced\/","title":{"rendered":"ICME2020 Grand Challenge: PAIR Competition Award Announced"},"content":{"rendered":"

2020-03-09<\/span><\/p>\n

 <\/p>\n

\"\"<\/a>
\n<\/span><\/p>\n

 <\/p>\n

The “ICME2020 Grand Challenge-Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries” co-sponsored by PAIR Labs and 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: Pixabay<\/a>)<\/span><\/p>\n

 <\/p>\n

\n

Award Winners<\/span><\/h3>\n
    \n
  • Champion: USTC-NELSLIP<\/span><\/li>\n
  • First Runner-up: BUPT_MCPRL<\/span><\/li>\n
  • Second Runner-up: DD_VISION<\/span><\/li>\n<\/ul>\n

     <\/p>\n<\/div>\n

    \n

    Special Awards<\/span><\/h3>\n
      \n
    • Best accuracy award: BUPT_MCPRL<\/span><\/li>\n
    • Best bicycle detection award: IBDO-AIOT<\/span><\/li>\n
    • Best scooter detection award: Deep Learner<\/span><\/li>\n<\/ul>\n

       <\/p>\n<\/div>\n

      \n

      Final Evaluation Result<\/span><\/h3>\n

      <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      \n

      group<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Name<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Accuracy %<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Model Size<\/span><\/b><\/span><\/p>\n

      (MByte<\/span>)<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Complexity<\/span><\/b><\/span><\/p>\n

      (GOPS\/frame)<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Speed<\/span><\/b><\/span><\/p>\n

      (ms<\/span>\/frame)<\/span><\/b><\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      mAP<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      bicycle<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      scooter<\/span><\/b><\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_01<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      BUPT_MCPRL<\/span><\/p>\n<\/td>\n

      		\n

      49.20<\/span><\/p>\n<\/td>\n

      		\n

      0.90<\/span><\/p>\n<\/td>\n

      		\n

      59.50<\/span><\/p>\n<\/td>\n

      		\n

      7.35<\/span><\/p>\n<\/td>\n

      		\n

      12.89<\/span><\/p>\n<\/td>\n

      		\n

      401.21<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_02<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      USTC-NELSLIP<\/span><\/p>\n<\/td>\n

      		\n

      44.60<\/span><\/p>\n<\/td>\n

      		\n

      0.20<\/span><\/p>\n<\/td>\n

      		\n

      56.00<\/span><\/p>\n<\/td>\n

      		\n

      6.04<\/span><\/p>\n<\/td>\n

      		\n

      11.16<\/span><\/p>\n<\/td>\n

      		\n

      141.8<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_03<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      DD_VISION<\/span><\/p>\n<\/td>\n

      		\n

      25.60<\/span><\/p>\n<\/td>\n

      		\n

      2.00<\/span><\/p>\n<\/td>\n

      		\n

      29.40<\/span><\/p>\n<\/td>\n

      		\n

      0.86<\/span><\/p>\n<\/td>\n

      		\n

      0.52<\/span><\/p>\n<\/td>\n

      		\n

      56.18<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_04<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Deep Learner<\/span><\/p>\n<\/td>\n

      		\n

      49.00<\/span><\/p>\n<\/td>\n

      		\n

      0.10<\/span><\/p>\n<\/td>\n

      		\n

      62.40<\/span><\/p>\n<\/td>\n

      		\n

      45.2<\/span><\/p>\n<\/td>\n

      		\n

      56.64<\/span><\/p>\n<\/td>\n

      		\n

      1560.43<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_06<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      nccu_vipl<\/span><\/span><\/p>\n<\/td>\n

      		\n

      38.70<\/span><\/p>\n<\/td>\n

      		\n

      0.00<\/span><\/p>\n<\/td>\n

      		\n

      48.20<\/span><\/p>\n<\/td>\n

      		\n

      187.27<\/span><\/p>\n<\/td>\n

      		\n

      285.7<\/span><\/p>\n<\/td>\n

      		\n

      287.11<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_07<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      IBDO-AIOT<\/span><\/p>\n<\/td>\n

      		\n

      47.00<\/span><\/p>\n<\/td>\n

      		\n

      12.00<\/span><\/p>\n<\/td>\n

      		\n

      53.70<\/span><\/p>\n<\/td>\n

      		\n

      215.49<\/span><\/p>\n<\/td>\n

      		\n

      99.46<\/span><\/p>\n<\/td>\n

      		\n

      567.65<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_08<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      ACVLab<\/span><\/span><\/p>\n<\/td>\n

      		\n

      41.10<\/span><\/p>\n<\/td>\n

      		\n

      0.10<\/span><\/p>\n<\/td>\n

      		\n

      49.90<\/span><\/p>\n<\/td>\n

      		\n

      87.46<\/span><\/p>\n<\/td>\n

      		\n

      31.82<\/span><\/p>\n<\/td>\n

      		\n

      696.72<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_010<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      \u8cc7\u5de5A<\/span><\/span><\/p>\n<\/td>\n

      		\n

      8.80<\/span><\/p>\n<\/td>\n

      		\n

      0.00<\/span><\/p>\n<\/td>\n

      		\n

      3.10<\/span><\/p>\n<\/td>\n

      		\n

      298.6<\/span><\/p>\n<\/td>\n

      		\n

      140.35<\/span><\/p>\n<\/td>\n

      		\n

      653.71<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_05<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Rock4Ever<\/span><\/p>\n<\/td>\n

      		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
      \n

      icme2020_09<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      jummy112<\/span><\/p>\n<\/td>\n

      		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
      \u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
      \n

      Final Score<\/span><\/h3>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
      \n

      group<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Name<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Partial Score<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Final Score<\/span><\/b><\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      mAP<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      model size<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      complexity<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      speed<\/span><\/b><\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_01<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      BUPT_MCPRL<\/span><\/p>\n<\/td>\n

      		\n

      25<\/span><\/p>\n<\/td>\n

      		\n

      24.46<\/span><\/p>\n<\/td>\n

      		\n

      23.92<\/span><\/p>\n<\/td>\n

      		\n

      19.27<\/span><\/p>\n<\/td>\n

      		\n

      92.64<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_02<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      USTC-NELSLIP<\/span><\/p>\n<\/td>\n

      		\n

      22.15<\/span><\/p>\n<\/td>\n

      		\n

      24.57<\/span><\/p>\n<\/td>\n

      		\n

      24.07<\/span><\/p>\n<\/td>\n

      		\n

      23.58<\/span><\/p>\n<\/td>\n

      		\n

      94.36<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_03<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      DD_VISION<\/span><\/p>\n<\/td>\n

      		\n

      10.4<\/span><\/p>\n<\/td>\n

      		\n

      25<\/span><\/p>\n<\/td>\n

      		\n

      25<\/span><\/p>\n<\/td>\n

      		\n

      25<\/span><\/p>\n<\/td>\n

      		\n

      85.40<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_04<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Deep Learner<\/span><\/p>\n<\/td>\n

      		\n

      24.88<\/span><\/p>\n<\/td>\n

      		\n

      21.28<\/span><\/p>\n<\/td>\n

      		\n

      20.08<\/span><\/p>\n<\/td>\n

      		\n

      0<\/span><\/p>\n<\/td>\n

      		\n

      66.23<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_06<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      nccu_vipl<\/span><\/span><\/p>\n<\/td>\n

      		\n

      18.5<\/span><\/p>\n<\/td>\n

      		\n

      9.35<\/span><\/p>\n<\/td>\n

      		\n

      0<\/span><\/p>\n<\/td>\n

      		\n

      21.16<\/span><\/p>\n<\/td>\n

      		\n

      49.01<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_07<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      IBDO-AIOT<\/span><\/p>\n<\/td>\n

      		\n

      23.64<\/span><\/p>\n<\/td>\n

      		\n

      6.98<\/span><\/p>\n<\/td>\n

      		\n

      16.33<\/span><\/p>\n<\/td>\n

      		\n

      16.5<\/span><\/p>\n<\/td>\n

      		\n

      63.44<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_08<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      ACVLab<\/span><\/span><\/p>\n<\/td>\n

      		\n

      19.99<\/span><\/p>\n<\/td>\n

      		\n

      17.73<\/span><\/p>\n<\/td>\n

      		\n

      22.26<\/span><\/p>\n<\/td>\n

      		\n

      14.35<\/span><\/p>\n<\/td>\n

      		\n

      74.33<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_010<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      \u8cc7\u5de5A<\/span><\/span><\/p>\n<\/td>\n

      		\n

      0<\/span><\/p>\n<\/td>\n

      		\n

      0<\/span><\/p>\n<\/td>\n

      		\n

      12.74<\/span><\/p>\n<\/td>\n

      		\n

      15.07<\/span><\/p>\n<\/td>\n

      		\n

      27.81<\/span><\/p>\n<\/td>\n<\/tr>\n

      \n

      icme2020_05<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      Rock4Ever<\/span><\/p>\n<\/td>\n

      		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
      \n

      icme2020_09<\/span><\/b><\/span><\/p>\n<\/td>\n

      		\n

      jummy112<\/span><\/p>\n<\/td>\n

      		\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

      2020-03-09     The “ICME2020 Grand Challenge-Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries” 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 […]<\/p>\n","protected":false},"author":12,"featured_media":3451,"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":[113],"tags":[],"class_list":["post-4110","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-n-en"],"_links":{"self":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/4110","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=4110"}],"version-history":[{"count":0,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/posts\/4110\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/"}],"wp:attachment":[{"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/media?parent=4110"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/categories?post=4110"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pairlabs.ai\/en\/wp-json\/wp\/v2\/tags?post=4110"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}