竞赛目的与意义
@@ -121,7 +125,7 @@
Open Vocabulary Detection Contest - 开放世界目标检测竞赛 2023
hosted by 360 AI Institute
-
+在各个竞赛团队的积极参与、中国图象图形学学会与360人工智能研究院的大力支持下，Open Vocabulary Detection Contest - 开放世界目标检测竞赛已经正式结束，在征集各个竞赛团队的许可后，我们将部分优胜团队的技术方案汇总并公开分享，详见以下链接：技术文档。
竞赛目的与意义
目标检测是计算机视觉中的核心任务之一，主要目的是让计算机可以自动识别图片中目标的类别，并标示出每个目标的位置。当前主流的目标检测方法主要针对闭集目标开发，即在整个任务前期需要对待检测目标进行类别定义，并进行人工数据标注，通过有监督模型训练使模型达到目标检测的目的。这一方式可以处理的待检测目标通常限定在几十类以内。但是当需要检测的目标类别增加到几千、万类时，上述方式在数据标注环节上已无法应对。与此同时，已训练模型也无法应对新的类别。当有新的类别出现时，需要手动进行标注并再次训练该模型，整体效率较低。
开放词集目标检测（Open Vocabulary Detection, OVD）提供了解决上述问题的新思路。借助于现有跨模态模型（CLIP[1]、ALIGN[2]、R2D2[3] 等）的泛化能力，OVD可以实现以下功能：
@@ -289,5 +293,5 @@ Keyboard Shortcuts
diff --git a/search/search_index.json b/search/search_index.json
index 867a089..232296e 100755
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-\\.]+"},"docs":[{"location":"","text":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023 hosted by 360 AI Institute \u7ade\u8d5b\u76ee\u7684\u4e0e\u610f\u4e49 \u76ee\u6807\u68c0\u6d4b\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u6838\u5fc3\u4efb\u52a1\u4e4b\u4e00\uff0c\u4e3b\u8981\u76ee\u7684\u662f\u8ba9\u8ba1\u7b97\u673a\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u7247\u4e2d\u76ee\u6807\u7684\u7c7b\u522b\uff0c\u5e76\u6807\u793a\u51fa\u6bcf\u4e2a\u76ee\u6807\u7684\u4f4d\u7f6e\u3002\u5f53\u524d\u4e3b\u6d41\u7684\u76ee\u6807\u68c0\u6d4b\u65b9\u6cd5\u4e3b\u8981\u9488\u5bf9\u95ed\u96c6\u76ee\u6807\u5f00\u53d1\uff0c\u5373\u5728\u6574\u4e2a\u4efb\u52a1\u524d\u671f\u9700\u8981\u5bf9\u5f85\u68c0\u6d4b\u76ee\u6807\u8fdb\u884c\u7c7b\u522b\u5b9a\u4e49\uff0c\u5e76\u8fdb\u884c\u4eba\u5de5\u6570\u636e\u6807\u6ce8\uff0c\u901a\u8fc7\u6709\u76d1\u7763\u6a21\u578b\u8bad\u7ec3\u4f7f\u6a21\u578b\u8fbe\u5230\u76ee\u6807\u68c0\u6d4b\u7684\u76ee\u7684\u3002\u8fd9\u4e00\u65b9\u5f0f\u53ef\u4ee5\u5904\u7406\u7684\u5f85\u68c0\u6d4b\u76ee\u6807\u901a\u5e38\u9650\u5b9a\u5728\u51e0\u5341\u7c7b\u4ee5\u5185\u3002\u4f46\u662f\u5f53\u9700\u8981\u68c0\u6d4b\u7684\u76ee\u6807\u7c7b\u522b\u589e\u52a0\u5230\u51e0\u5343\u3001\u4e07\u7c7b\u65f6\uff0c\u4e0a\u8ff0\u65b9\u5f0f\u5728\u6570\u636e\u6807\u6ce8\u73af\u8282\u4e0a\u5df2\u65e0\u6cd5\u5e94\u5bf9\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u5df2\u8bad\u7ec3\u6a21\u578b\u4e5f\u65e0\u6cd5\u5e94\u5bf9\u65b0\u7684\u7c7b\u522b\u3002\u5f53\u6709\u65b0\u7684\u7c7b\u522b\u51fa\u73b0\u65f6\uff0c\u9700\u8981\u624b\u52a8\u8fdb\u884c\u6807\u6ce8\u5e76\u518d\u6b21\u8bad\u7ec3\u8be5\u6a21\u578b\uff0c\u6574\u4f53\u6548\u7387\u8f83\u4f4e\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\uff08Open Vocabulary Detection, OVD\uff09\u63d0\u4f9b\u4e86\u89e3\u51b3\u4e0a\u8ff0\u95ee\u9898\u7684\u65b0\u601d\u8def\u3002\u501f\u52a9\u4e8e\u73b0\u6709\u8de8\u6a21\u6001\u6a21\u578b\uff08CLIP[1]\u3001ALIGN[2]\u3001 R2D2 [3] \u7b49\uff09\u7684\u6cdb\u5316\u80fd\u529b\uff0cOVD\u53ef\u4ee5\u5b9e\u73b0\u4ee5\u4e0b\u529f\u80fd\uff1a 1\uff09\u5bf9\u5df2\u5b9a\u4e49\u7c7b\u522b\u7684few shot\u68c0\u6d4b\uff1b 2\uff09\u5bf9\u672a\u5b9a\u4e49\u7c7b\u522b\u7684zero-shot\u68c0\u6d4b\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\u6709\u671b\u6210\u4e3a\u672a\u6765\u76ee\u6807\u68c0\u6d4b\u7b97\u6cd5\u5f00\u53d1\u7684\u65b0\u8303\u5f0f\u3002 \u4efb\u52a1\u8bbe\u7f6e \u53c2\u8d5b\u8005\u5c06\u8fd0\u7528OVD\u76f8\u5173\u7684\u65b9\u6cd5\uff0c\u5bf9\u56fe\u50cf\u4e2d\u7684\u5546\u54c1\u76ee\u6807\u8fdb\u884c\u68c0\u6d4b\u3002\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53cabbox\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u76ee\u6807\u7c7b\u522b\u6709\u4e24\u7c7b\uff1abase\u7c7b\u548cnovel\u7c7b\u3002\u7c7b\u522b\u5747\u4e3a\u4e2d\u6587\u5546\u54c1\u8bcd\u7ec4\u3002base\u7c7b\u7684\u76ee\u6807\u63d0\u4f9b\u5c11\u91cf\u5df2\u6807\u6ce8\u7684\u8bad\u7ec3\u6837\u672c\uff0cnovel\u7c7b\u7684\u76ee\u6807\u5219\u6ca1\u6709\u8bad\u7ec3\u6837\u672c\u3002\u8bc4\u6d4b\u5206\u522b\u5728base\u7c7b\u7684\u6d4b\u8bd5\u96c6\u548cnovel\u7c7b\u7684\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\uff0c\u8bc4\u6d4b\u6307\u6807\u4e3anovel\u548cbase\u7c7b\u7684mAP@50\uff0c\u7ade\u8d5b\u6309\u7167novel\u548cbase\u7c7b\u522b\u7684\u6574\u4f53mAP@50\u6392\u5e8f\u3002 \u5956\u9879\u8bbe\u7f6e\u548c\u5956\u52b1\u65b9\u6cd5 \u4e00\u7b49\u5956\uff1a1\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d13\u4e07\u5143 \u4e8c\u7b49\u5956\uff1a2\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54041\u4e07\u5143 \u4e09\u7b49\u5956\uff1a3\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54045\u5343\u5143 \u51b3\u8d5b\u83b7\u80dc\u961f\u4f0d\u5c06\u5728 ICIG2023\u5927\u4f1a \u4e0a\u8fdb\u884c\u65b9\u6848\u5206\u4eab\u6f14\u8bb2 \u4e3b\u8981\u65f6\u95f4\u8282\u70b9 \u9636\u6bb5 \u65f6\u95f4 \u8bf4\u660e \u7ebf\u4e0a\u62a5\u540d 4/12 ~ 7/30 \u62a5\u540d\u6ce8\u518c \u521d\u8d5b 4/12 ~ 7/30 - \u521d\u8d5b\u8bc4\u5ba1\u548c\u590d\u8d5b\u5165\u56f4\u7ed3\u679c\u516c\u5e03 7/30 ~ 8/10 - \u590d\u8d5b 8/10 ~ 8/30 - \u590d\u8d5b\u8bc4\u5ba1\u548c\u7ed3\u679c\u516c\u5e03 8/30 ~ 9/10 - \u9881\u5956 9/22 ~ 9/24 - \u7ade\u8d5b\u53c2\u4e0e\u8005\u8981\u6c42 \u53c2\u8d5b\u8005\u53ef\u4ee5\u81ea\u7531\u7ec4\u961f\uff0c\u6bcf\u961f\u4e0d\u9650\u4eba\u6570 \u6bcf\u4f4d\u53c2\u8d5b\u8005\u53ea\u80fd\u53c2\u52a0\u4e00\u53ea\u961f\u4f0d \u521d\u8d5b\u548c\u590d\u8d5b\u671f\u95f4\uff0c\u6bcf\u4e2a\u961f\u4f0d\u5355\u65e5\u9650\u5236\u63d0\u4ea42\u6b21\u7ed3\u679c \u7ade\u8d5b\u7ec4\u7ec7 \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b\u7531360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662 \u8054\u5408\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a \u5171\u540c\u4e3e\u529e\u3002 360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u6210\u7acb\u4e8e2015\u5e74\uff0c\u6211\u4eec\u805a\u7126\u4e8e\u7814\u53d1\u4e1a\u754c\u9886\u5148\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u6df1\u5ea6\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u3001\u8bed\u97f3\u8bed\u4e49\u4ea4\u4e92\u3001\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u3001\u673a\u5668\u4eba\u8fd0\u52a8\u7b49\u4eba\u5de5\u667a\u80fd\u6280\u672f\uff0c\u5e76\u5e94\u7528\u4e8e\u667a\u6167\u7269\u8054\u7f51\uff08IOT\uff09\u3001\u667a\u80fd\u5b89\u5168\u5927\u6570\u636e\u3001\u4e92\u8054\u7f51\u4fe1\u606f\u5206\u53d1\u3001\u4f01\u4e1a\u6570\u5b57\u5316\u3001\u667a\u80fd\u6c7d\u8f66\u7b49\u591a\u79cd\u573a\u666f\u3002 \u56e2\u961f\u591a\u6b21\u5728\u56fd\u9645\u5927\u8d5b\u4e2d\u53d6\u5f97\u4f18\u5f02\u6210\u7ee9\uff0c\u627f\u62c5\u8fc7\u56fd\u5bb6\u548c\u5317\u4eac\u5e02\u591a\u4e2a\u91cd\u70b9\u653b\u5173\u9879\u76ee\uff0c\u53c2\u4e0e\u5efa\u8bbe\u56fd\u5bb6\u7ea7\u91cd\u70b9\u5927\u6570\u636e\u5de5\u7a0b\u5b9e\u9a8c\u5ba4\uff0c\u53c2\u4e0e\u5efa\u8bbe\u7684\u5b89\u5168\u5927\u8111\u5165\u9009\u56fd\u5bb6\u65b0\u4e00\u4ee3\u4eba\u5de5\u667a\u80fd\u5f00\u653e\u521b\u65b0\u5e73\u53f0\u3002\u6211\u4eec\u6253\u9020\u7684\u7b97\u6cd5\u548c\u670d\u52a1\uff0c\u5df2\u5e94\u7528\u4e8e\u591a\u6761\u4e1a\u52a1\u7ebf\uff0c\u652f\u6301\u5343\u4e07\u7ea7\u786c\u4ef6\u8bbe\u5907\uff0c\u4ebf\u7ea7\u7528\u6237\uff0c\u4ea7\u751f\u7684\u6570\u636e\u91cf\u8fbe\u5343\u4ebf\u89c4\u6a21\u3002 \u56e2\u961f\u591a\u540d\u6210\u5458\u6bd5\u4e1a\u4e8e\u65b0\u52a0\u5761\u56fd\u7acb\u3001\u6e05\u534e\u3001\u5317\u5927\u7b49\u56fd\u5185\u5916\u77e5\u540d\u9ad8\u6821\uff0c\u5927\u591a\u6570\u5c0f\u4f19\u4f34\u66fe\u4efb\u804c\u4e8e\u5fae\u8f6f\u3001\u767e\u5ea6\u3001\u963f\u91cc\u7b49\u4e1a\u754c\u77e5\u540d\u516c\u53f8\u3002\u6211\u4eec\u7684\u4ef7\u503c\u89c2\u662f\u201c\u7814\u7a76\u4e1a\u754c\u4e00\u6d41\u6280\u672f\uff0c\u521b\u9020\u4ea7\u4e1a\u843d\u5730\u4ef7\u503c\u201d\u3002 [1] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748\u20138763. PMLR, 2021. [2] C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. V. Le, Y. Sung, Z. Li, and T. Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, 2021. [3] Xie C, Cai H, Song J, et al. Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework[J]. arXiv preprint arXiv:2205.03860, 2022.","title":"\u8d5b\u9898\u4ecb\u7ecd"},{"location":"#open-vocabulary-detection-contest-2023","text":"hosted by 360 AI Institute","title":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023"},{"location":"#_1","text":"\u76ee\u6807\u68c0\u6d4b\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u6838\u5fc3\u4efb\u52a1\u4e4b\u4e00\uff0c\u4e3b\u8981\u76ee\u7684\u662f\u8ba9\u8ba1\u7b97\u673a\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u7247\u4e2d\u76ee\u6807\u7684\u7c7b\u522b\uff0c\u5e76\u6807\u793a\u51fa\u6bcf\u4e2a\u76ee\u6807\u7684\u4f4d\u7f6e\u3002\u5f53\u524d\u4e3b\u6d41\u7684\u76ee\u6807\u68c0\u6d4b\u65b9\u6cd5\u4e3b\u8981\u9488\u5bf9\u95ed\u96c6\u76ee\u6807\u5f00\u53d1\uff0c\u5373\u5728\u6574\u4e2a\u4efb\u52a1\u524d\u671f\u9700\u8981\u5bf9\u5f85\u68c0\u6d4b\u76ee\u6807\u8fdb\u884c\u7c7b\u522b\u5b9a\u4e49\uff0c\u5e76\u8fdb\u884c\u4eba\u5de5\u6570\u636e\u6807\u6ce8\uff0c\u901a\u8fc7\u6709\u76d1\u7763\u6a21\u578b\u8bad\u7ec3\u4f7f\u6a21\u578b\u8fbe\u5230\u76ee\u6807\u68c0\u6d4b\u7684\u76ee\u7684\u3002\u8fd9\u4e00\u65b9\u5f0f\u53ef\u4ee5\u5904\u7406\u7684\u5f85\u68c0\u6d4b\u76ee\u6807\u901a\u5e38\u9650\u5b9a\u5728\u51e0\u5341\u7c7b\u4ee5\u5185\u3002\u4f46\u662f\u5f53\u9700\u8981\u68c0\u6d4b\u7684\u76ee\u6807\u7c7b\u522b\u589e\u52a0\u5230\u51e0\u5343\u3001\u4e07\u7c7b\u65f6\uff0c\u4e0a\u8ff0\u65b9\u5f0f\u5728\u6570\u636e\u6807\u6ce8\u73af\u8282\u4e0a\u5df2\u65e0\u6cd5\u5e94\u5bf9\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u5df2\u8bad\u7ec3\u6a21\u578b\u4e5f\u65e0\u6cd5\u5e94\u5bf9\u65b0\u7684\u7c7b\u522b\u3002\u5f53\u6709\u65b0\u7684\u7c7b\u522b\u51fa\u73b0\u65f6\uff0c\u9700\u8981\u624b\u52a8\u8fdb\u884c\u6807\u6ce8\u5e76\u518d\u6b21\u8bad\u7ec3\u8be5\u6a21\u578b\uff0c\u6574\u4f53\u6548\u7387\u8f83\u4f4e\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\uff08Open Vocabulary Detection, OVD\uff09\u63d0\u4f9b\u4e86\u89e3\u51b3\u4e0a\u8ff0\u95ee\u9898\u7684\u65b0\u601d\u8def\u3002\u501f\u52a9\u4e8e\u73b0\u6709\u8de8\u6a21\u6001\u6a21\u578b\uff08CLIP[1]\u3001ALIGN[2]\u3001 R2D2 [3] \u7b49\uff09\u7684\u6cdb\u5316\u80fd\u529b\uff0cOVD\u53ef\u4ee5\u5b9e\u73b0\u4ee5\u4e0b\u529f\u80fd\uff1a 1\uff09\u5bf9\u5df2\u5b9a\u4e49\u7c7b\u522b\u7684few shot\u68c0\u6d4b\uff1b 2\uff09\u5bf9\u672a\u5b9a\u4e49\u7c7b\u522b\u7684zero-shot\u68c0\u6d4b\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\u6709\u671b\u6210\u4e3a\u672a\u6765\u76ee\u6807\u68c0\u6d4b\u7b97\u6cd5\u5f00\u53d1\u7684\u65b0\u8303\u5f0f\u3002","title":"\u7ade\u8d5b\u76ee\u7684\u4e0e\u610f\u4e49"},{"location":"#_2","text":"\u53c2\u8d5b\u8005\u5c06\u8fd0\u7528OVD\u76f8\u5173\u7684\u65b9\u6cd5\uff0c\u5bf9\u56fe\u50cf\u4e2d\u7684\u5546\u54c1\u76ee\u6807\u8fdb\u884c\u68c0\u6d4b\u3002\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53cabbox\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u76ee\u6807\u7c7b\u522b\u6709\u4e24\u7c7b\uff1abase\u7c7b\u548cnovel\u7c7b\u3002\u7c7b\u522b\u5747\u4e3a\u4e2d\u6587\u5546\u54c1\u8bcd\u7ec4\u3002base\u7c7b\u7684\u76ee\u6807\u63d0\u4f9b\u5c11\u91cf\u5df2\u6807\u6ce8\u7684\u8bad\u7ec3\u6837\u672c\uff0cnovel\u7c7b\u7684\u76ee\u6807\u5219\u6ca1\u6709\u8bad\u7ec3\u6837\u672c\u3002\u8bc4\u6d4b\u5206\u522b\u5728base\u7c7b\u7684\u6d4b\u8bd5\u96c6\u548cnovel\u7c7b\u7684\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\uff0c\u8bc4\u6d4b\u6307\u6807\u4e3anovel\u548cbase\u7c7b\u7684mAP@50\uff0c\u7ade\u8d5b\u6309\u7167novel\u548cbase\u7c7b\u522b\u7684\u6574\u4f53mAP@50\u6392\u5e8f\u3002","title":"\u4efb\u52a1\u8bbe\u7f6e"},{"location":"#_3","text":"\u4e00\u7b49\u5956\uff1a1\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d13\u4e07\u5143 \u4e8c\u7b49\u5956\uff1a2\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54041\u4e07\u5143 \u4e09\u7b49\u5956\uff1a3\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54045\u5343\u5143 \u51b3\u8d5b\u83b7\u80dc\u961f\u4f0d\u5c06\u5728 ICIG2023\u5927\u4f1a \u4e0a\u8fdb\u884c\u65b9\u6848\u5206\u4eab\u6f14\u8bb2","title":"\u5956\u9879\u8bbe\u7f6e\u548c\u5956\u52b1\u65b9\u6cd5"},{"location":"#_4","text":"\u9636\u6bb5 \u65f6\u95f4 \u8bf4\u660e \u7ebf\u4e0a\u62a5\u540d 4/12 ~ 7/30 \u62a5\u540d\u6ce8\u518c \u521d\u8d5b 4/12 ~ 7/30 - \u521d\u8d5b\u8bc4\u5ba1\u548c\u590d\u8d5b\u5165\u56f4\u7ed3\u679c\u516c\u5e03 7/30 ~ 8/10 - \u590d\u8d5b 8/10 ~ 8/30 - \u590d\u8d5b\u8bc4\u5ba1\u548c\u7ed3\u679c\u516c\u5e03 8/30 ~ 9/10 - \u9881\u5956 9/22 ~ 9/24 -","title":"\u4e3b\u8981\u65f6\u95f4\u8282\u70b9"},{"location":"#_5","text":"\u53c2\u8d5b\u8005\u53ef\u4ee5\u81ea\u7531\u7ec4\u961f\uff0c\u6bcf\u961f\u4e0d\u9650\u4eba\u6570 \u6bcf\u4f4d\u53c2\u8d5b\u8005\u53ea\u80fd\u53c2\u52a0\u4e00\u53ea\u961f\u4f0d \u521d\u8d5b\u548c\u590d\u8d5b\u671f\u95f4\uff0c\u6bcf\u4e2a\u961f\u4f0d\u5355\u65e5\u9650\u5236\u63d0\u4ea42\u6b21\u7ed3\u679c","title":"\u7ade\u8d5b\u53c2\u4e0e\u8005\u8981\u6c42"},{"location":"#_6","text":"\u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b\u7531360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662 \u8054\u5408\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a \u5171\u540c\u4e3e\u529e\u3002 360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u6210\u7acb\u4e8e2015\u5e74\uff0c\u6211\u4eec\u805a\u7126\u4e8e\u7814\u53d1\u4e1a\u754c\u9886\u5148\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u6df1\u5ea6\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u3001\u8bed\u97f3\u8bed\u4e49\u4ea4\u4e92\u3001\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u3001\u673a\u5668\u4eba\u8fd0\u52a8\u7b49\u4eba\u5de5\u667a\u80fd\u6280\u672f\uff0c\u5e76\u5e94\u7528\u4e8e\u667a\u6167\u7269\u8054\u7f51\uff08IOT\uff09\u3001\u667a\u80fd\u5b89\u5168\u5927\u6570\u636e\u3001\u4e92\u8054\u7f51\u4fe1\u606f\u5206\u53d1\u3001\u4f01\u4e1a\u6570\u5b57\u5316\u3001\u667a\u80fd\u6c7d\u8f66\u7b49\u591a\u79cd\u573a\u666f\u3002 \u56e2\u961f\u591a\u6b21\u5728\u56fd\u9645\u5927\u8d5b\u4e2d\u53d6\u5f97\u4f18\u5f02\u6210\u7ee9\uff0c\u627f\u62c5\u8fc7\u56fd\u5bb6\u548c\u5317\u4eac\u5e02\u591a\u4e2a\u91cd\u70b9\u653b\u5173\u9879\u76ee\uff0c\u53c2\u4e0e\u5efa\u8bbe\u56fd\u5bb6\u7ea7\u91cd\u70b9\u5927\u6570\u636e\u5de5\u7a0b\u5b9e\u9a8c\u5ba4\uff0c\u53c2\u4e0e\u5efa\u8bbe\u7684\u5b89\u5168\u5927\u8111\u5165\u9009\u56fd\u5bb6\u65b0\u4e00\u4ee3\u4eba\u5de5\u667a\u80fd\u5f00\u653e\u521b\u65b0\u5e73\u53f0\u3002\u6211\u4eec\u6253\u9020\u7684\u7b97\u6cd5\u548c\u670d\u52a1\uff0c\u5df2\u5e94\u7528\u4e8e\u591a\u6761\u4e1a\u52a1\u7ebf\uff0c\u652f\u6301\u5343\u4e07\u7ea7\u786c\u4ef6\u8bbe\u5907\uff0c\u4ebf\u7ea7\u7528\u6237\uff0c\u4ea7\u751f\u7684\u6570\u636e\u91cf\u8fbe\u5343\u4ebf\u89c4\u6a21\u3002 \u56e2\u961f\u591a\u540d\u6210\u5458\u6bd5\u4e1a\u4e8e\u65b0\u52a0\u5761\u56fd\u7acb\u3001\u6e05\u534e\u3001\u5317\u5927\u7b49\u56fd\u5185\u5916\u77e5\u540d\u9ad8\u6821\uff0c\u5927\u591a\u6570\u5c0f\u4f19\u4f34\u66fe\u4efb\u804c\u4e8e\u5fae\u8f6f\u3001\u767e\u5ea6\u3001\u963f\u91cc\u7b49\u4e1a\u754c\u77e5\u540d\u516c\u53f8\u3002\u6211\u4eec\u7684\u4ef7\u503c\u89c2\u662f\u201c\u7814\u7a76\u4e1a\u754c\u4e00\u6d41\u6280\u672f\uff0c\u521b\u9020\u4ea7\u4e1a\u843d\u5730\u4ef7\u503c\u201d\u3002 [1] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748\u20138763. PMLR, 2021. [2] C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. V. Le, Y. Sung, Z. Li, and T. Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, 2021. [3] Xie C, Cai H, Song J, et al. Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework[J]. arXiv preprint arXiv:2205.03860, 2022.","title":"\u7ade\u8d5b\u7ec4\u7ec7"},{"location":"data/","text":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023 hosted by 360 AI Institute \u53c2\u8d5b\u6ce8\u518c \u6ce8\u518c\u94fe\u63a5\uff1a \u7528\u6237\u6ce8\u518c \u6ce8\u518c\u4fe1\u606f\u5305\u62ec\uff1a\u53c2\u8d5b\u961f\u4f0d\u540d\u79f0 \u53c2\u8d5b\u961f\u4f0d\u4ecb\u7ecd \u53c2\u8d5b\u961f\u4f0d\u6210\u5458\u4ecb\u7ecd\uff08\u59d3\u540d\uff0c\u673a\u6784\u540d\uff09 \u53c2\u8d5b\u961f\u4f0d\u8054\u7cfb\u90ae\u7bb1 \u6ce8\u518c\u6210\u529f\u540e\u4e3b\u529e\u65b9\u5c06\u5f80\u8be5\u90ae\u7bb1\u53d1\u9001\u5f53\u524d\u961f\u4f0d\u72ec\u5c5e\u7684UUID\uff0c\u7528\u4e8e\u540e\u7eed\u7ed3\u679c\u63d0\u4ea4\uff0c\u8bf7\u59a5\u5584\u4fdd\u5b58\u8be5UUID\uff0c\u5207\u52ff\u6cc4\u9732 \u6570\u636e\u4e0b\u8f7d \u53c2\u8d5b\u6ce8\u518c\u540e\uff0c\u6570\u636e\u5c06\u4ee5\u90ae\u4ef6\u7684\u5f62\u5f0f\u53d1\u9001\u94fe\u63a5\u5230\u53c2\u8d5b\u8005\u90ae\u7bb1\uff0c\u8bf7\u6ce8\u610f\u67e5\u6536 \u6570\u636e\u8bf4\u660e \u672c\u9879\u4efb\u52a1\u6db5\u76d6\u4e86\u670d\u88c5\u3001\u6570\u7801\u4ea7\u54c1\u7b49\u4f17\u591a\u5546\u54c1\u7c7b\u76ee\uff0c\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53ca\u5bf9\u5e94\u7684\u68c0\u6d4b\u6846\u6807\u6ce8\u4fe1\u606f\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u6807\u6ce8\u6570\u636e\u7684\u793a\u4f8b\u5982\u4e0b\uff1a \u521d\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_pre_contest.tgz\u548cjson_pre_contest.tgz\uff0c\u5305\u62ec\uff1a ./train\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./train.json\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u6807\u6ce8\u4fe1\u606f\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b3\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aannotations, images, categories - annotations - {'bbox': [115.640625, 187.353515625, 665.3892211914062, 462.7544860839844], 'iscrowd': 0, 'image_id': 1, 'attributes': {'occluded': False}, 'category_id': 1, 'id': 1, 'segmentation': [[115.640625,187.353515625, 781.0298461914062, 187.353515625, 781.0298461914062, 650.1080017089844, 115.640625, 650.1080017089844]], 'area': 307911.8470982518} - images - {'license': 0, 'flickr_url': '', 'coco_url': '', 'height': 800, 'id': 1, 'file_name': '1d1dd4dc32b6abdb.jpg', 'width': 800, 'date_captured': 0} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 233, 'supercategory': '', 'name': '\u5973\u58eb\u4e1d\u5dfe', 'split': 'seen'} ./test\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./test.json\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u50cf\u4fe1\u606f\u548c\u7c7b\u522b\u6c47\u603b\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b2\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aimages, categories - image - {'id': 1, 'file_name': 'a841257bff7b85b3.jpg', 'flickr_url': '', 'license': 0, 'height': 800, 'width': 800, 'date_captured': 0, 'coco_url': ''} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 466, 'supercategory': '', 'name': '\u5973\u58eb\u6cf3\u8863', 'split': 'unseen'} \u5176\u4e2d'split'\u5b57\u6bb5\u7528\u6765\u5212\u5206\u7c7b\u522b\uff0cseen\u4ee3\u8868\u8bad\u7ec3\u96c6\u51fa\u73b0\u8fc7\u7684base\u7c7b\u522b\uff0cunseen\u4ee3\u8868\u6d4b\u8bd5\u96c6\u4e2d\u7684novel\u7c7b\u522b \u51b3\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_final_contest.tgz\u548cjson_final_contest.tgz\uff0c\u6587\u4ef6\u7ec4\u7ec7\u5f62\u5f0f\u4e0e\u521d\u8d5b\u76f8\u540c\uff0c\u5728\u51b3\u8d5b\u5f00\u59cb\u540e\u53d1\u9001\u5230\u51b3\u8d5b\u53c2\u8d5b\u8005\u90ae\u7bb1 \u4f7f\u7528\u6761\u6b3e \u8be5\u6570\u636e\u53ea\u80fd\u7528\u4e8e\u975e\u5546\u4e1a\u7814\u7a76\u548c\u5b66\u672f\u6559\u80b2 \u6211\u4eec\u7981\u6b62\u7528\u6237\u5206\u53d1\u6570\u636e\u96c6\u6216\u4fee\u6539\u7248\u672c \u53c2\u8d5b\u8005\u5bf9\u4f7f\u7528\u8d5b\u9898\u6570\u636e\u627f\u62c5\u5168\u90e8\u8d23\u4efb\u3002\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u5947\u864e360\u53ca\u5176\u5173\u8054\u516c\u53f8\u3001\u6216\u5176\u8463\u4e8b\u3001\u96c7\u5458\u3001\u4ee3\u7406\u4eba\u3001\u5408\u4f5c\u4f19\u4f34\u4ee5\u53ca\u4f9b\u5e94\u5546\u5747\u5bf9\u672c\u7f51\u7ad9\u3001\u6570\u636e\u4e0d\u8d1f\u6709\u8d23\u4efb \u5728\u672c\u7f51\u7ad9\u4e0a\u6216\u901a\u8fc7\u672c\u7f51\u7ad9\u8fdb\u884c\u7684\u8bbf\u95ee\u53ca\u6240\u6709\u76f8\u5173\u6d3b\u52a8\u5747\u53d7\u4e2d\u534e\u4eba\u6c11\u5171\u548c\u56fd\u6cd5\u5f8b\u7ba1\u8f96\u5e76\u53d7\u5176\u89e3\u91ca\u3002","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"data/#open-vocabulary-detection-contest-2023","text":"hosted by 360 AI Institute","title":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023"},{"location":"data/#_1","text":"\u6ce8\u518c\u94fe\u63a5\uff1a \u7528\u6237\u6ce8\u518c \u6ce8\u518c\u4fe1\u606f\u5305\u62ec\uff1a\u53c2\u8d5b\u961f\u4f0d\u540d\u79f0 \u53c2\u8d5b\u961f\u4f0d\u4ecb\u7ecd \u53c2\u8d5b\u961f\u4f0d\u6210\u5458\u4ecb\u7ecd\uff08\u59d3\u540d\uff0c\u673a\u6784\u540d\uff09 \u53c2\u8d5b\u961f\u4f0d\u8054\u7cfb\u90ae\u7bb1 \u6ce8\u518c\u6210\u529f\u540e\u4e3b\u529e\u65b9\u5c06\u5f80\u8be5\u90ae\u7bb1\u53d1\u9001\u5f53\u524d\u961f\u4f0d\u72ec\u5c5e\u7684UUID\uff0c\u7528\u4e8e\u540e\u7eed\u7ed3\u679c\u63d0\u4ea4\uff0c\u8bf7\u59a5\u5584\u4fdd\u5b58\u8be5UUID\uff0c\u5207\u52ff\u6cc4\u9732","title":"\u53c2\u8d5b\u6ce8\u518c"},{"location":"data/#_2","text":"\u53c2\u8d5b\u6ce8\u518c\u540e\uff0c\u6570\u636e\u5c06\u4ee5\u90ae\u4ef6\u7684\u5f62\u5f0f\u53d1\u9001\u94fe\u63a5\u5230\u53c2\u8d5b\u8005\u90ae\u7bb1\uff0c\u8bf7\u6ce8\u610f\u67e5\u6536","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"data/#_3","text":"\u672c\u9879\u4efb\u52a1\u6db5\u76d6\u4e86\u670d\u88c5\u3001\u6570\u7801\u4ea7\u54c1\u7b49\u4f17\u591a\u5546\u54c1\u7c7b\u76ee\uff0c\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53ca\u5bf9\u5e94\u7684\u68c0\u6d4b\u6846\u6807\u6ce8\u4fe1\u606f\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u6807\u6ce8\u6570\u636e\u7684\u793a\u4f8b\u5982\u4e0b\uff1a \u521d\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_pre_contest.tgz\u548cjson_pre_contest.tgz\uff0c\u5305\u62ec\uff1a ./train\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./train.json\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u6807\u6ce8\u4fe1\u606f\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b3\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aannotations, images, categories - annotations - {'bbox': [115.640625, 187.353515625, 665.3892211914062, 462.7544860839844], 'iscrowd': 0, 'image_id': 1, 'attributes': {'occluded': False}, 'category_id': 1, 'id': 1, 'segmentation': [[115.640625,187.353515625, 781.0298461914062, 187.353515625, 781.0298461914062, 650.1080017089844, 115.640625, 650.1080017089844]], 'area': 307911.8470982518} - images - {'license': 0, 'flickr_url': '', 'coco_url': '', 'height': 800, 'id': 1, 'file_name': '1d1dd4dc32b6abdb.jpg', 'width': 800, 'date_captured': 0} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 233, 'supercategory': '', 'name': '\u5973\u58eb\u4e1d\u5dfe', 'split': 'seen'} ./test\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./test.json\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u50cf\u4fe1\u606f\u548c\u7c7b\u522b\u6c47\u603b\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b2\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aimages, categories - image - {'id': 1, 'file_name': 'a841257bff7b85b3.jpg', 'flickr_url': '', 'license': 0, 'height': 800, 'width': 800, 'date_captured': 0, 'coco_url': ''} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 466, 'supercategory': '', 'name': '\u5973\u58eb\u6cf3\u8863', 'split': 'unseen'} \u5176\u4e2d'split'\u5b57\u6bb5\u7528\u6765\u5212\u5206\u7c7b\u522b\uff0cseen\u4ee3\u8868\u8bad\u7ec3\u96c6\u51fa\u73b0\u8fc7\u7684base\u7c7b\u522b\uff0cunseen\u4ee3\u8868\u6d4b\u8bd5\u96c6\u4e2d\u7684novel\u7c7b\u522b \u51b3\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_final_contest.tgz\u548cjson_final_contest.tgz\uff0c\u6587\u4ef6\u7ec4\u7ec7\u5f62\u5f0f\u4e0e\u521d\u8d5b\u76f8\u540c\uff0c\u5728\u51b3\u8d5b\u5f00\u59cb\u540e\u53d1\u9001\u5230\u51b3\u8d5b\u53c2\u8d5b\u8005\u90ae\u7bb1","title":"\u6570\u636e\u8bf4\u660e"},{"location":"data/#_4","text":"\u8be5\u6570\u636e\u53ea\u80fd\u7528\u4e8e\u975e\u5546\u4e1a\u7814\u7a76\u548c\u5b66\u672f\u6559\u80b2 \u6211\u4eec\u7981\u6b62\u7528\u6237\u5206\u53d1\u6570\u636e\u96c6\u6216\u4fee\u6539\u7248\u672c \u53c2\u8d5b\u8005\u5bf9\u4f7f\u7528\u8d5b\u9898\u6570\u636e\u627f\u62c5\u5168\u90e8\u8d23\u4efb\u3002\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u5947\u864e360\u53ca\u5176\u5173\u8054\u516c\u53f8\u3001\u6216\u5176\u8463\u4e8b\u3001\u96c7\u5458\u3001\u4ee3\u7406\u4eba\u3001\u5408\u4f5c\u4f19\u4f34\u4ee5\u53ca\u4f9b\u5e94\u5546\u5747\u5bf9\u672c\u7f51\u7ad9\u3001\u6570\u636e\u4e0d\u8d1f\u6709\u8d23\u4efb \u5728\u672c\u7f51\u7ad9\u4e0a\u6216\u901a\u8fc7\u672c\u7f51\u7ad9\u8fdb\u884c\u7684\u8bbf\u95ee\u53ca\u6240\u6709\u76f8\u5173\u6d3b\u52a8\u5747\u53d7\u4e2d\u534e\u4eba\u6c11\u5171\u548c\u56fd\u6cd5\u5f8b\u7ba1\u8f96\u5e76\u53d7\u5176\u89e3\u91ca\u3002","title":"\u4f7f\u7528\u6761\u6b3e"},{"location":"leaderboard/","text":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023 hosted by 360 AI Institute Leaderboard \u6bcf\u65e5\u66f4\u65b0 \u590d\u8d5b \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd final 56.604 52.12 54.362 2 STAR OVDEA 52.28 53.176 52.728 3 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 \u518d\u7ed9KFC\u4e00\u6b21\u673a\u4f1a 50.075 54.158 52.117 4 OVD f-huge 47.885 47.146 47.516 5 wzmwzr Test1 47.635 42.649 45.142 6 \u84dd\u8272\u95ea IFEAsT30_100_P1-3++ 43.317 45.593 44.455 7 \u8102\u73af 0810_1 32.497 35.234 33.865 8 \u7b97\u6cd5\u5168\u90fd\u961f 4 21.797 40.068 30.932 9 OVD-Research 0716 15.222 31.961 23.591 10 OVD en 9.2004 29.57 19.385 \u521d\u8d5b\u6210\u7ee9 \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd bl4.16.0.8 55.392 51.131 53.262 2 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 kfc\u6c38\u5b58 47.687 52.248 49.968 3 STAR OVDC 49.359 48.541 48.95 4 wzmwzr Test1 48.501 42.679 45.59 5 lucky test_v16 40.706 49.302 45.004 6 OVD A1 40.253 46.985 43.619 7 \u84dd\u8272\u95ea B_I_F_E_A_g_50-3+ 33.984 38.129 36.057 8 \u8102\u73af 0724_1 32.828 36.77 34.799 9 \u7b97\u6cd5\u5168\u90fd\u961f \u6587\u5fc3\u4e00\u8a0014.0 33.429 35.805 34.617 10 OVD en 21.246 36.291 28.768 11 OVD-Research 0716 16.064 33.793 24.929 12 Lethe test0 12.316 35.368 23.842 13 \u5b87\u667a\u6ce2\u5bb6\u65cf G_L_F_M_di 21.232 25.345 23.288 14 mclab_415 v2 19.626 25.488 22.557 15 yahoo Test2 19.718 24.882 22.3 16 \u534e\u4e2d\u79d1\u6280\u5927\u5b66\u95ee\u53f7\u55b5\u55b5 \u55b5^3 15.436 28.36 21.898 17 testaaaaaa RegionCLIP-L1 16.224 24.967 20.596 18 XDU-Wolf vldet_test_all 10.58 26.964 18.772 19 \u6c6a\u961f\u5927\u5934\u5175 open_2 3.3975 33.835 18.616 20 OPEN-HUST openhust_v3B 16.793 18.084 17.438 21 OVD\u5c0f\u5206\u961f RCF_V1 7.3306 26.086 16.708 22 \u5f00\u653e\u4e16\u754c\u6d4b\u4e0d\u961f model_3 0.16206 33.046 16.604 23 \u5c11\u8c31 wzy_yyds 16.217 16.495 16.356 24 sysu_zhigong 2202wn 19.792 12.876 16.334 25 sysu_abcd 2201 20.035 12.465 16.25 26 temp001 22015 19.826 12.441 16.133 27 \u901a\u4e50\u961f last_1 12.968 17.778 15.373 28 \u539f\u795e\uff0c\u542f\u52a8\uff01 \u795e\u91cc\u7eeb\u534e 2.912 26.313 14.612 29 208\u961f zs208 12.236 12.979 12.608 30 \u6da7\u5cb1\u5b97\u70bc\u4e39\u95e8 Test_5 5.7197 18.968 12.344 31 \u8d5e\u7f8e\u4e07\u673a\u4e4b\u795e clip2 8.5958 9.5801 9.0879 32 \u5bf9\u5bf9\u961f test2 3.5378 2.0395 2.7887 33 HCP_OpenDetection 28 3.1386 1.6192 2.3789 34 \u9a6c\u6cfd\u5e73 Haas 1.1995 1.3711 1.2853 35 NVIDIA Yes MCGA_G1 1.5468 0.84399 1.1954 36 \u9876\u74dc\u74dc test2 1.0626 0.95454 1.0086 37 \u7237\u4eec\u8981\u6218\u6597 v2 1.0901 0.16163 0.62586 38 QAQ yo 0.0 0.13617 0.068086 39 \u54ce\uff0c\u5c31\u8fd9 v1 5.1809e-06 0.0044949 0.00225 \u7ed3\u679c\u4e0a\u4f20 \u6ce8\uff1a\u63d0\u4ea4\u6587\u4ef6\u9650\u5236\u4e3azip\u683c\u5f0f\uff0czip\u6587\u4ef6\u5185\u4ec5\u5305\u542bjson\u6587\u4ef6\uff0c\u5355\u4e2aUUID\u6bcf\u65e5\u63d0\u4ea4\u4e0a\u9650\u4e3a2\u6b21\u3002\u7528\u6237\u4e0a\u4f20\u540e\uff0c\u6b21\u65e5\u4f1a\u4ee5\u90ae\u4ef6\u5f62\u5f0f\u544a\u77e5\u7ed3\u679c\u3002 json\u6587\u4ef6\u5185\u4e3b\u4f53\u4e3a\u4e00\u4e2aList\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5b50\u9879\u5e94\u8be5\u4e3aDict,\u5177\u4f53\u4e3a\u4ee5\u4e0b\u683c\u5f0f\uff1a {'image_id': 19805, 'category_id': 1, 'bbox': [276.3232421875, 123.5966796875, 240.05747985839844, 602.2131958007812], 'score': 0.99} \u4e0a\u4f20\u7ed3\u679c \u7ade\u8d5b\u52a8\u6001 news \u57fa\u4e8e\u53c2\u8d5b\u961f\u4f0d\u6570\u76ee\u3001\u961f\u4f0d\u63d0\u4ea4\u9891\u7387\u3001\u521d\u8d5b\u6210\u7ee9\u8003\u91cf\u4e0e\u6bd4\u8d5b\u5956\u91d1\u7684\u8bbe\u7f6e\uff0c\u8fdb\u5165\u590d\u8d5b\u961f\u4f0d\u4e3a\u521d\u8d5b\u6210\u7ee9\u7684\u524d12\u652f\uff0c\u5373\u53c2\u4e0e\u590d\u8d5b\u961f\u4f0d\u6570\u91cf\u4e3a\u53ef\u83b7\u5956\u961f\u4f0d\u6570\u91cf\u7684200%\u3002\u8bf7\u6ce8\u610f\uff0c\u590d\u8d5b\u5c06\u4e8e8\u670810\u65e5\u51c6\u65f6\u5f00\u542f\uff0c\u6301\u7eed\u65f6\u95f4\u4e3a20\u5929\uff0c\u5c06\u4e8e8\u670830\u65e524\u70b9\u5173\u95ed\u63d0\u4ea4\u7aef\u53e3\uff01 \u8bf7\u6ce8\u610f\uff01\u6211\u4eec\u5df2\u5c06\u521d\u8d5b\u6ce8\u518c\u65f6\u95f4\u4e0e\u6bd4\u8d5b\u65f6\u95f4\u65f6\u95f4\u622a\u6b62\u65e5\u671f\u5ef6\u957f\u4e00\u4e2a\u6708\uff0c\u5c06\u539f\u5148\u76846\u670830\u65e5\u63a8\u540e\u81f37\u670830\u65e5\u3002 \u793e\u533a\u8ba8\u8bba\u548c\u7b54\u7591 \u5982\u6709\u6bd4\u8d5b\u76f8\u5173\u95ee\u9898\uff0c\u53ef\u5728 Issues \u8fdb\u884c\u63d0\u95ee\u8ba8\u8bba","title":"Leaderboard"},{"location":"leaderboard/#open-vocabulary-detection-contest-2023","text":"hosted by 360 AI Institute","title":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023"},{"location":"leaderboard/#leaderboard","text":"\u6bcf\u65e5\u66f4\u65b0 \u590d\u8d5b \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd final 56.604 52.12 54.362 2 STAR OVDEA 52.28 53.176 52.728 3 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 \u518d\u7ed9KFC\u4e00\u6b21\u673a\u4f1a 50.075 54.158 52.117 4 OVD f-huge 47.885 47.146 47.516 5 wzmwzr Test1 47.635 42.649 45.142 6 \u84dd\u8272\u95ea IFEAsT30_100_P1-3++ 43.317 45.593 44.455 7 \u8102\u73af 0810_1 32.497 35.234 33.865 8 \u7b97\u6cd5\u5168\u90fd\u961f 4 21.797 40.068 30.932 9 OVD-Research 0716 15.222 31.961 23.591 10 OVD en 9.2004 29.57 19.385 \u521d\u8d5b\u6210\u7ee9 \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd bl4.16.0.8 55.392 51.131 53.262 2 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 kfc\u6c38\u5b58 47.687 52.248 49.968 3 STAR OVDC 49.359 48.541 48.95 4 wzmwzr Test1 48.501 42.679 45.59 5 lucky test_v16 40.706 49.302 45.004 6 OVD A1 40.253 46.985 43.619 7 \u84dd\u8272\u95ea B_I_F_E_A_g_50-3+ 33.984 38.129 36.057 8 \u8102\u73af 0724_1 32.828 36.77 34.799 9 \u7b97\u6cd5\u5168\u90fd\u961f \u6587\u5fc3\u4e00\u8a0014.0 33.429 35.805 34.617 10 OVD en 21.246 36.291 28.768 11 OVD-Research 0716 16.064 33.793 24.929 12 Lethe test0 12.316 35.368 23.842 13 \u5b87\u667a\u6ce2\u5bb6\u65cf G_L_F_M_di 21.232 25.345 23.288 14 mclab_415 v2 19.626 25.488 22.557 15 yahoo Test2 19.718 24.882 22.3 16 \u534e\u4e2d\u79d1\u6280\u5927\u5b66\u95ee\u53f7\u55b5\u55b5 \u55b5^3 15.436 28.36 21.898 17 testaaaaaa RegionCLIP-L1 16.224 24.967 20.596 18 XDU-Wolf vldet_test_all 10.58 26.964 18.772 19 \u6c6a\u961f\u5927\u5934\u5175 open_2 3.3975 33.835 18.616 20 OPEN-HUST openhust_v3B 16.793 18.084 17.438 21 OVD\u5c0f\u5206\u961f RCF_V1 7.3306 26.086 16.708 22 \u5f00\u653e\u4e16\u754c\u6d4b\u4e0d\u961f model_3 0.16206 33.046 16.604 23 \u5c11\u8c31 wzy_yyds 16.217 16.495 16.356 24 sysu_zhigong 2202wn 19.792 12.876 16.334 25 sysu_abcd 2201 20.035 12.465 16.25 26 temp001 22015 19.826 12.441 16.133 27 \u901a\u4e50\u961f last_1 12.968 17.778 15.373 28 \u539f\u795e\uff0c\u542f\u52a8\uff01 \u795e\u91cc\u7eeb\u534e 2.912 26.313 14.612 29 208\u961f zs208 12.236 12.979 12.608 30 \u6da7\u5cb1\u5b97\u70bc\u4e39\u95e8 Test_5 5.7197 18.968 12.344 31 \u8d5e\u7f8e\u4e07\u673a\u4e4b\u795e clip2 8.5958 9.5801 9.0879 32 \u5bf9\u5bf9\u961f test2 3.5378 2.0395 2.7887 33 HCP_OpenDetection 28 3.1386 1.6192 2.3789 34 \u9a6c\u6cfd\u5e73 Haas 1.1995 1.3711 1.2853 35 NVIDIA Yes MCGA_G1 1.5468 0.84399 1.1954 36 \u9876\u74dc\u74dc test2 1.0626 0.95454 1.0086 37 \u7237\u4eec\u8981\u6218\u6597 v2 1.0901 0.16163 0.62586 38 QAQ yo 0.0 0.13617 0.068086 39 \u54ce\uff0c\u5c31\u8fd9 v1 5.1809e-06 0.0044949 0.00225","title":"Leaderboard"},{"location":"leaderboard/#_1","text":"\u6ce8\uff1a\u63d0\u4ea4\u6587\u4ef6\u9650\u5236\u4e3azip\u683c\u5f0f\uff0czip\u6587\u4ef6\u5185\u4ec5\u5305\u542bjson\u6587\u4ef6\uff0c\u5355\u4e2aUUID\u6bcf\u65e5\u63d0\u4ea4\u4e0a\u9650\u4e3a2\u6b21\u3002\u7528\u6237\u4e0a\u4f20\u540e\uff0c\u6b21\u65e5\u4f1a\u4ee5\u90ae\u4ef6\u5f62\u5f0f\u544a\u77e5\u7ed3\u679c\u3002 json\u6587\u4ef6\u5185\u4e3b\u4f53\u4e3a\u4e00\u4e2aList\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5b50\u9879\u5e94\u8be5\u4e3aDict,\u5177\u4f53\u4e3a\u4ee5\u4e0b\u683c\u5f0f\uff1a {'image_id': 19805, 'category_id': 1, 'bbox': [276.3232421875, 123.5966796875, 240.05747985839844, 602.2131958007812], 'score': 0.99} \u4e0a\u4f20\u7ed3\u679c","title":"\u7ed3\u679c\u4e0a\u4f20"},{"location":"leaderboard/#_2","text":"news \u57fa\u4e8e\u53c2\u8d5b\u961f\u4f0d\u6570\u76ee\u3001\u961f\u4f0d\u63d0\u4ea4\u9891\u7387\u3001\u521d\u8d5b\u6210\u7ee9\u8003\u91cf\u4e0e\u6bd4\u8d5b\u5956\u91d1\u7684\u8bbe\u7f6e\uff0c\u8fdb\u5165\u590d\u8d5b\u961f\u4f0d\u4e3a\u521d\u8d5b\u6210\u7ee9\u7684\u524d12\u652f\uff0c\u5373\u53c2\u4e0e\u590d\u8d5b\u961f\u4f0d\u6570\u91cf\u4e3a\u53ef\u83b7\u5956\u961f\u4f0d\u6570\u91cf\u7684200%\u3002\u8bf7\u6ce8\u610f\uff0c\u590d\u8d5b\u5c06\u4e8e8\u670810\u65e5\u51c6\u65f6\u5f00\u542f\uff0c\u6301\u7eed\u65f6\u95f4\u4e3a20\u5929\uff0c\u5c06\u4e8e8\u670830\u65e524\u70b9\u5173\u95ed\u63d0\u4ea4\u7aef\u53e3\uff01 \u8bf7\u6ce8\u610f\uff01\u6211\u4eec\u5df2\u5c06\u521d\u8d5b\u6ce8\u518c\u65f6\u95f4\u4e0e\u6bd4\u8d5b\u65f6\u95f4\u65f6\u95f4\u622a\u6b62\u65e5\u671f\u5ef6\u957f\u4e00\u4e2a\u6708\uff0c\u5c06\u539f\u5148\u76846\u670830\u65e5\u63a8\u540e\u81f37\u670830\u65e5\u3002","title":"\u7ade\u8d5b\u52a8\u6001"},{"location":"leaderboard/#_3","text":"\u5982\u6709\u6bd4\u8d5b\u76f8\u5173\u95ee\u9898\uff0c\u53ef\u5728 Issues \u8fdb\u884c\u63d0\u95ee\u8ba8\u8bba","title":"\u793e\u533a\u8ba8\u8bba\u548c\u7b54\u7591"}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-\\.]+"},"docs":[{"location":"","text":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023 hosted by 360 AI Institute \u5728\u5404\u4e2a\u7ade\u8d5b\u56e2\u961f\u7684\u79ef\u6781\u53c2\u4e0e\u3001\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a\u4e0e360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u7684\u5927\u529b\u652f\u6301\u4e0b\uff0cOpen Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b\u5df2\u7ecf\u6b63\u5f0f\u7ed3\u675f\uff0c\u5728\u5f81\u96c6\u5404\u4e2a\u7ade\u8d5b\u56e2\u961f\u7684\u8bb8\u53ef\u540e\uff0c\u6211\u4eec\u5c06\u90e8\u5206\u4f18\u80dc\u56e2\u961f\u7684\u6280\u672f\u65b9\u6848\u6c47\u603b\u5e76\u516c\u5f00\u5206\u4eab\uff0c\u8be6\u89c1\u4ee5\u4e0b\u94fe\u63a5\uff1a \u6280\u672f\u6587\u6863 \u3002 \u7ade\u8d5b\u76ee\u7684\u4e0e\u610f\u4e49 \u76ee\u6807\u68c0\u6d4b\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u6838\u5fc3\u4efb\u52a1\u4e4b\u4e00\uff0c\u4e3b\u8981\u76ee\u7684\u662f\u8ba9\u8ba1\u7b97\u673a\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u7247\u4e2d\u76ee\u6807\u7684\u7c7b\u522b\uff0c\u5e76\u6807\u793a\u51fa\u6bcf\u4e2a\u76ee\u6807\u7684\u4f4d\u7f6e\u3002\u5f53\u524d\u4e3b\u6d41\u7684\u76ee\u6807\u68c0\u6d4b\u65b9\u6cd5\u4e3b\u8981\u9488\u5bf9\u95ed\u96c6\u76ee\u6807\u5f00\u53d1\uff0c\u5373\u5728\u6574\u4e2a\u4efb\u52a1\u524d\u671f\u9700\u8981\u5bf9\u5f85\u68c0\u6d4b\u76ee\u6807\u8fdb\u884c\u7c7b\u522b\u5b9a\u4e49\uff0c\u5e76\u8fdb\u884c\u4eba\u5de5\u6570\u636e\u6807\u6ce8\uff0c\u901a\u8fc7\u6709\u76d1\u7763\u6a21\u578b\u8bad\u7ec3\u4f7f\u6a21\u578b\u8fbe\u5230\u76ee\u6807\u68c0\u6d4b\u7684\u76ee\u7684\u3002\u8fd9\u4e00\u65b9\u5f0f\u53ef\u4ee5\u5904\u7406\u7684\u5f85\u68c0\u6d4b\u76ee\u6807\u901a\u5e38\u9650\u5b9a\u5728\u51e0\u5341\u7c7b\u4ee5\u5185\u3002\u4f46\u662f\u5f53\u9700\u8981\u68c0\u6d4b\u7684\u76ee\u6807\u7c7b\u522b\u589e\u52a0\u5230\u51e0\u5343\u3001\u4e07\u7c7b\u65f6\uff0c\u4e0a\u8ff0\u65b9\u5f0f\u5728\u6570\u636e\u6807\u6ce8\u73af\u8282\u4e0a\u5df2\u65e0\u6cd5\u5e94\u5bf9\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u5df2\u8bad\u7ec3\u6a21\u578b\u4e5f\u65e0\u6cd5\u5e94\u5bf9\u65b0\u7684\u7c7b\u522b\u3002\u5f53\u6709\u65b0\u7684\u7c7b\u522b\u51fa\u73b0\u65f6\uff0c\u9700\u8981\u624b\u52a8\u8fdb\u884c\u6807\u6ce8\u5e76\u518d\u6b21\u8bad\u7ec3\u8be5\u6a21\u578b\uff0c\u6574\u4f53\u6548\u7387\u8f83\u4f4e\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\uff08Open Vocabulary Detection, OVD\uff09\u63d0\u4f9b\u4e86\u89e3\u51b3\u4e0a\u8ff0\u95ee\u9898\u7684\u65b0\u601d\u8def\u3002\u501f\u52a9\u4e8e\u73b0\u6709\u8de8\u6a21\u6001\u6a21\u578b\uff08CLIP[1]\u3001ALIGN[2]\u3001 R2D2 [3] \u7b49\uff09\u7684\u6cdb\u5316\u80fd\u529b\uff0cOVD\u53ef\u4ee5\u5b9e\u73b0\u4ee5\u4e0b\u529f\u80fd\uff1a 1\uff09\u5bf9\u5df2\u5b9a\u4e49\u7c7b\u522b\u7684few shot\u68c0\u6d4b\uff1b 2\uff09\u5bf9\u672a\u5b9a\u4e49\u7c7b\u522b\u7684zero-shot\u68c0\u6d4b\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\u6709\u671b\u6210\u4e3a\u672a\u6765\u76ee\u6807\u68c0\u6d4b\u7b97\u6cd5\u5f00\u53d1\u7684\u65b0\u8303\u5f0f\u3002 \u4efb\u52a1\u8bbe\u7f6e \u53c2\u8d5b\u8005\u5c06\u8fd0\u7528OVD\u76f8\u5173\u7684\u65b9\u6cd5\uff0c\u5bf9\u56fe\u50cf\u4e2d\u7684\u5546\u54c1\u76ee\u6807\u8fdb\u884c\u68c0\u6d4b\u3002\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53cabbox\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u76ee\u6807\u7c7b\u522b\u6709\u4e24\u7c7b\uff1abase\u7c7b\u548cnovel\u7c7b\u3002\u7c7b\u522b\u5747\u4e3a\u4e2d\u6587\u5546\u54c1\u8bcd\u7ec4\u3002base\u7c7b\u7684\u76ee\u6807\u63d0\u4f9b\u5c11\u91cf\u5df2\u6807\u6ce8\u7684\u8bad\u7ec3\u6837\u672c\uff0cnovel\u7c7b\u7684\u76ee\u6807\u5219\u6ca1\u6709\u8bad\u7ec3\u6837\u672c\u3002\u8bc4\u6d4b\u5206\u522b\u5728base\u7c7b\u7684\u6d4b\u8bd5\u96c6\u548cnovel\u7c7b\u7684\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\uff0c\u8bc4\u6d4b\u6307\u6807\u4e3anovel\u548cbase\u7c7b\u7684mAP@50\uff0c\u7ade\u8d5b\u6309\u7167novel\u548cbase\u7c7b\u522b\u7684\u6574\u4f53mAP@50\u6392\u5e8f\u3002 \u5956\u9879\u8bbe\u7f6e\u548c\u5956\u52b1\u65b9\u6cd5 \u4e00\u7b49\u5956\uff1a1\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d13\u4e07\u5143 \u4e8c\u7b49\u5956\uff1a2\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54041\u4e07\u5143 \u4e09\u7b49\u5956\uff1a3\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54045\u5343\u5143 \u51b3\u8d5b\u83b7\u80dc\u961f\u4f0d\u5c06\u5728 ICIG2023\u5927\u4f1a \u4e0a\u8fdb\u884c\u65b9\u6848\u5206\u4eab\u6f14\u8bb2 \u4e3b\u8981\u65f6\u95f4\u8282\u70b9 \u9636\u6bb5 \u65f6\u95f4 \u8bf4\u660e \u7ebf\u4e0a\u62a5\u540d 4/12 ~ 7/30 \u62a5\u540d\u6ce8\u518c \u521d\u8d5b 4/12 ~ 7/30 - \u521d\u8d5b\u8bc4\u5ba1\u548c\u590d\u8d5b\u5165\u56f4\u7ed3\u679c\u516c\u5e03 7/30 ~ 8/10 - \u590d\u8d5b 8/10 ~ 8/30 - \u590d\u8d5b\u8bc4\u5ba1\u548c\u7ed3\u679c\u516c\u5e03 8/30 ~ 9/10 - \u9881\u5956 9/22 ~ 9/24 - \u7ade\u8d5b\u53c2\u4e0e\u8005\u8981\u6c42 \u53c2\u8d5b\u8005\u53ef\u4ee5\u81ea\u7531\u7ec4\u961f\uff0c\u6bcf\u961f\u4e0d\u9650\u4eba\u6570 \u6bcf\u4f4d\u53c2\u8d5b\u8005\u53ea\u80fd\u53c2\u52a0\u4e00\u53ea\u961f\u4f0d \u521d\u8d5b\u548c\u590d\u8d5b\u671f\u95f4\uff0c\u6bcf\u4e2a\u961f\u4f0d\u5355\u65e5\u9650\u5236\u63d0\u4ea42\u6b21\u7ed3\u679c \u7ade\u8d5b\u7ec4\u7ec7 \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b\u7531360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662 \u8054\u5408\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a \u5171\u540c\u4e3e\u529e\u3002 360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u6210\u7acb\u4e8e2015\u5e74\uff0c\u6211\u4eec\u805a\u7126\u4e8e\u7814\u53d1\u4e1a\u754c\u9886\u5148\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u6df1\u5ea6\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u3001\u8bed\u97f3\u8bed\u4e49\u4ea4\u4e92\u3001\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u3001\u673a\u5668\u4eba\u8fd0\u52a8\u7b49\u4eba\u5de5\u667a\u80fd\u6280\u672f\uff0c\u5e76\u5e94\u7528\u4e8e\u667a\u6167\u7269\u8054\u7f51\uff08IOT\uff09\u3001\u667a\u80fd\u5b89\u5168\u5927\u6570\u636e\u3001\u4e92\u8054\u7f51\u4fe1\u606f\u5206\u53d1\u3001\u4f01\u4e1a\u6570\u5b57\u5316\u3001\u667a\u80fd\u6c7d\u8f66\u7b49\u591a\u79cd\u573a\u666f\u3002 \u56e2\u961f\u591a\u6b21\u5728\u56fd\u9645\u5927\u8d5b\u4e2d\u53d6\u5f97\u4f18\u5f02\u6210\u7ee9\uff0c\u627f\u62c5\u8fc7\u56fd\u5bb6\u548c\u5317\u4eac\u5e02\u591a\u4e2a\u91cd\u70b9\u653b\u5173\u9879\u76ee\uff0c\u53c2\u4e0e\u5efa\u8bbe\u56fd\u5bb6\u7ea7\u91cd\u70b9\u5927\u6570\u636e\u5de5\u7a0b\u5b9e\u9a8c\u5ba4\uff0c\u53c2\u4e0e\u5efa\u8bbe\u7684\u5b89\u5168\u5927\u8111\u5165\u9009\u56fd\u5bb6\u65b0\u4e00\u4ee3\u4eba\u5de5\u667a\u80fd\u5f00\u653e\u521b\u65b0\u5e73\u53f0\u3002\u6211\u4eec\u6253\u9020\u7684\u7b97\u6cd5\u548c\u670d\u52a1\uff0c\u5df2\u5e94\u7528\u4e8e\u591a\u6761\u4e1a\u52a1\u7ebf\uff0c\u652f\u6301\u5343\u4e07\u7ea7\u786c\u4ef6\u8bbe\u5907\uff0c\u4ebf\u7ea7\u7528\u6237\uff0c\u4ea7\u751f\u7684\u6570\u636e\u91cf\u8fbe\u5343\u4ebf\u89c4\u6a21\u3002 \u56e2\u961f\u591a\u540d\u6210\u5458\u6bd5\u4e1a\u4e8e\u65b0\u52a0\u5761\u56fd\u7acb\u3001\u6e05\u534e\u3001\u5317\u5927\u7b49\u56fd\u5185\u5916\u77e5\u540d\u9ad8\u6821\uff0c\u5927\u591a\u6570\u5c0f\u4f19\u4f34\u66fe\u4efb\u804c\u4e8e\u5fae\u8f6f\u3001\u767e\u5ea6\u3001\u963f\u91cc\u7b49\u4e1a\u754c\u77e5\u540d\u516c\u53f8\u3002\u6211\u4eec\u7684\u4ef7\u503c\u89c2\u662f\u201c\u7814\u7a76\u4e1a\u754c\u4e00\u6d41\u6280\u672f\uff0c\u521b\u9020\u4ea7\u4e1a\u843d\u5730\u4ef7\u503c\u201d\u3002 [1] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748\u20138763. PMLR, 2021. [2] C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. V. Le, Y. Sung, Z. Li, and T. Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, 2021. [3] Xie C, Cai H, Song J, et al. Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework[J]. arXiv preprint arXiv:2205.03860, 2022.","title":"\u8d5b\u9898\u4ecb\u7ecd"},{"location":"#open-vocabulary-detection-contest-2023","text":"hosted by 360 AI Institute","title":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023"},{"location":"#360open-vocabulary-detection-contest-","text":"","title":"\u5728\u5404\u4e2a\u7ade\u8d5b\u56e2\u961f\u7684\u79ef\u6781\u53c2\u4e0e\u3001\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a\u4e0e360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u7684\u5927\u529b\u652f\u6301\u4e0b\uff0cOpen Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b\u5df2\u7ecf\u6b63\u5f0f\u7ed3\u675f\uff0c\u5728\u5f81\u96c6\u5404\u4e2a\u7ade\u8d5b\u56e2\u961f\u7684\u8bb8\u53ef\u540e\uff0c\u6211\u4eec\u5c06\u90e8\u5206\u4f18\u80dc\u56e2\u961f\u7684\u6280\u672f\u65b9\u6848\u6c47\u603b\u5e76\u516c\u5f00\u5206\u4eab\uff0c\u8be6\u89c1\u4ee5\u4e0b\u94fe\u63a5\uff1a\u6280\u672f\u6587\u6863\u3002"},{"location":"#_1","text":"\u76ee\u6807\u68c0\u6d4b\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u6838\u5fc3\u4efb\u52a1\u4e4b\u4e00\uff0c\u4e3b\u8981\u76ee\u7684\u662f\u8ba9\u8ba1\u7b97\u673a\u53ef\u4ee5\u81ea\u52a8\u8bc6\u522b\u56fe\u7247\u4e2d\u76ee\u6807\u7684\u7c7b\u522b\uff0c\u5e76\u6807\u793a\u51fa\u6bcf\u4e2a\u76ee\u6807\u7684\u4f4d\u7f6e\u3002\u5f53\u524d\u4e3b\u6d41\u7684\u76ee\u6807\u68c0\u6d4b\u65b9\u6cd5\u4e3b\u8981\u9488\u5bf9\u95ed\u96c6\u76ee\u6807\u5f00\u53d1\uff0c\u5373\u5728\u6574\u4e2a\u4efb\u52a1\u524d\u671f\u9700\u8981\u5bf9\u5f85\u68c0\u6d4b\u76ee\u6807\u8fdb\u884c\u7c7b\u522b\u5b9a\u4e49\uff0c\u5e76\u8fdb\u884c\u4eba\u5de5\u6570\u636e\u6807\u6ce8\uff0c\u901a\u8fc7\u6709\u76d1\u7763\u6a21\u578b\u8bad\u7ec3\u4f7f\u6a21\u578b\u8fbe\u5230\u76ee\u6807\u68c0\u6d4b\u7684\u76ee\u7684\u3002\u8fd9\u4e00\u65b9\u5f0f\u53ef\u4ee5\u5904\u7406\u7684\u5f85\u68c0\u6d4b\u76ee\u6807\u901a\u5e38\u9650\u5b9a\u5728\u51e0\u5341\u7c7b\u4ee5\u5185\u3002\u4f46\u662f\u5f53\u9700\u8981\u68c0\u6d4b\u7684\u76ee\u6807\u7c7b\u522b\u589e\u52a0\u5230\u51e0\u5343\u3001\u4e07\u7c7b\u65f6\uff0c\u4e0a\u8ff0\u65b9\u5f0f\u5728\u6570\u636e\u6807\u6ce8\u73af\u8282\u4e0a\u5df2\u65e0\u6cd5\u5e94\u5bf9\u3002\u4e0e\u6b64\u540c\u65f6\uff0c\u5df2\u8bad\u7ec3\u6a21\u578b\u4e5f\u65e0\u6cd5\u5e94\u5bf9\u65b0\u7684\u7c7b\u522b\u3002\u5f53\u6709\u65b0\u7684\u7c7b\u522b\u51fa\u73b0\u65f6\uff0c\u9700\u8981\u624b\u52a8\u8fdb\u884c\u6807\u6ce8\u5e76\u518d\u6b21\u8bad\u7ec3\u8be5\u6a21\u578b\uff0c\u6574\u4f53\u6548\u7387\u8f83\u4f4e\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\uff08Open Vocabulary Detection, OVD\uff09\u63d0\u4f9b\u4e86\u89e3\u51b3\u4e0a\u8ff0\u95ee\u9898\u7684\u65b0\u601d\u8def\u3002\u501f\u52a9\u4e8e\u73b0\u6709\u8de8\u6a21\u6001\u6a21\u578b\uff08CLIP[1]\u3001ALIGN[2]\u3001 R2D2 [3] \u7b49\uff09\u7684\u6cdb\u5316\u80fd\u529b\uff0cOVD\u53ef\u4ee5\u5b9e\u73b0\u4ee5\u4e0b\u529f\u80fd\uff1a 1\uff09\u5bf9\u5df2\u5b9a\u4e49\u7c7b\u522b\u7684few shot\u68c0\u6d4b\uff1b 2\uff09\u5bf9\u672a\u5b9a\u4e49\u7c7b\u522b\u7684zero-shot\u68c0\u6d4b\u3002 \u5f00\u653e\u8bcd\u96c6\u76ee\u6807\u68c0\u6d4b\u6709\u671b\u6210\u4e3a\u672a\u6765\u76ee\u6807\u68c0\u6d4b\u7b97\u6cd5\u5f00\u53d1\u7684\u65b0\u8303\u5f0f\u3002","title":"\u7ade\u8d5b\u76ee\u7684\u4e0e\u610f\u4e49"},{"location":"#_2","text":"\u53c2\u8d5b\u8005\u5c06\u8fd0\u7528OVD\u76f8\u5173\u7684\u65b9\u6cd5\uff0c\u5bf9\u56fe\u50cf\u4e2d\u7684\u5546\u54c1\u76ee\u6807\u8fdb\u884c\u68c0\u6d4b\u3002\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53cabbox\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u76ee\u6807\u7c7b\u522b\u6709\u4e24\u7c7b\uff1abase\u7c7b\u548cnovel\u7c7b\u3002\u7c7b\u522b\u5747\u4e3a\u4e2d\u6587\u5546\u54c1\u8bcd\u7ec4\u3002base\u7c7b\u7684\u76ee\u6807\u63d0\u4f9b\u5c11\u91cf\u5df2\u6807\u6ce8\u7684\u8bad\u7ec3\u6837\u672c\uff0cnovel\u7c7b\u7684\u76ee\u6807\u5219\u6ca1\u6709\u8bad\u7ec3\u6837\u672c\u3002\u8bc4\u6d4b\u5206\u522b\u5728base\u7c7b\u7684\u6d4b\u8bd5\u96c6\u548cnovel\u7c7b\u7684\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\uff0c\u8bc4\u6d4b\u6307\u6807\u4e3anovel\u548cbase\u7c7b\u7684mAP@50\uff0c\u7ade\u8d5b\u6309\u7167novel\u548cbase\u7c7b\u522b\u7684\u6574\u4f53mAP@50\u6392\u5e8f\u3002","title":"\u4efb\u52a1\u8bbe\u7f6e"},{"location":"#_3","text":"\u4e00\u7b49\u5956\uff1a1\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d13\u4e07\u5143 \u4e8c\u7b49\u5956\uff1a2\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54041\u4e07\u5143 \u4e09\u7b49\u5956\uff1a3\u652f\u53c2\u8d5b\u961f\u4f0d\uff0c\u5956\u91d1\u54045\u5343\u5143 \u51b3\u8d5b\u83b7\u80dc\u961f\u4f0d\u5c06\u5728 ICIG2023\u5927\u4f1a \u4e0a\u8fdb\u884c\u65b9\u6848\u5206\u4eab\u6f14\u8bb2","title":"\u5956\u9879\u8bbe\u7f6e\u548c\u5956\u52b1\u65b9\u6cd5"},{"location":"#_4","text":"\u9636\u6bb5 \u65f6\u95f4 \u8bf4\u660e \u7ebf\u4e0a\u62a5\u540d 4/12 ~ 7/30 \u62a5\u540d\u6ce8\u518c \u521d\u8d5b 4/12 ~ 7/30 - \u521d\u8d5b\u8bc4\u5ba1\u548c\u590d\u8d5b\u5165\u56f4\u7ed3\u679c\u516c\u5e03 7/30 ~ 8/10 - \u590d\u8d5b 8/10 ~ 8/30 - \u590d\u8d5b\u8bc4\u5ba1\u548c\u7ed3\u679c\u516c\u5e03 8/30 ~ 9/10 - \u9881\u5956 9/22 ~ 9/24 -","title":"\u4e3b\u8981\u65f6\u95f4\u8282\u70b9"},{"location":"#_5","text":"\u53c2\u8d5b\u8005\u53ef\u4ee5\u81ea\u7531\u7ec4\u961f\uff0c\u6bcf\u961f\u4e0d\u9650\u4eba\u6570 \u6bcf\u4f4d\u53c2\u8d5b\u8005\u53ea\u80fd\u53c2\u52a0\u4e00\u53ea\u961f\u4f0d \u521d\u8d5b\u548c\u590d\u8d5b\u671f\u95f4\uff0c\u6bcf\u4e2a\u961f\u4f0d\u5355\u65e5\u9650\u5236\u63d0\u4ea42\u6b21\u7ed3\u679c","title":"\u7ade\u8d5b\u53c2\u4e0e\u8005\u8981\u6c42"},{"location":"#_6","text":"\u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b\u7531360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662 \u8054\u5408\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a \u5171\u540c\u4e3e\u529e\u3002 360\u4eba\u5de5\u667a\u80fd\u7814\u7a76\u9662\u6210\u7acb\u4e8e2015\u5e74\uff0c\u6211\u4eec\u805a\u7126\u4e8e\u7814\u53d1\u4e1a\u754c\u9886\u5148\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u3001\u6df1\u5ea6\u81ea\u7136\u8bed\u8a00\u7406\u89e3\u3001\u8bed\u97f3\u8bed\u4e49\u4ea4\u4e92\u3001\u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u3001\u673a\u5668\u4eba\u8fd0\u52a8\u7b49\u4eba\u5de5\u667a\u80fd\u6280\u672f\uff0c\u5e76\u5e94\u7528\u4e8e\u667a\u6167\u7269\u8054\u7f51\uff08IOT\uff09\u3001\u667a\u80fd\u5b89\u5168\u5927\u6570\u636e\u3001\u4e92\u8054\u7f51\u4fe1\u606f\u5206\u53d1\u3001\u4f01\u4e1a\u6570\u5b57\u5316\u3001\u667a\u80fd\u6c7d\u8f66\u7b49\u591a\u79cd\u573a\u666f\u3002 \u56e2\u961f\u591a\u6b21\u5728\u56fd\u9645\u5927\u8d5b\u4e2d\u53d6\u5f97\u4f18\u5f02\u6210\u7ee9\uff0c\u627f\u62c5\u8fc7\u56fd\u5bb6\u548c\u5317\u4eac\u5e02\u591a\u4e2a\u91cd\u70b9\u653b\u5173\u9879\u76ee\uff0c\u53c2\u4e0e\u5efa\u8bbe\u56fd\u5bb6\u7ea7\u91cd\u70b9\u5927\u6570\u636e\u5de5\u7a0b\u5b9e\u9a8c\u5ba4\uff0c\u53c2\u4e0e\u5efa\u8bbe\u7684\u5b89\u5168\u5927\u8111\u5165\u9009\u56fd\u5bb6\u65b0\u4e00\u4ee3\u4eba\u5de5\u667a\u80fd\u5f00\u653e\u521b\u65b0\u5e73\u53f0\u3002\u6211\u4eec\u6253\u9020\u7684\u7b97\u6cd5\u548c\u670d\u52a1\uff0c\u5df2\u5e94\u7528\u4e8e\u591a\u6761\u4e1a\u52a1\u7ebf\uff0c\u652f\u6301\u5343\u4e07\u7ea7\u786c\u4ef6\u8bbe\u5907\uff0c\u4ebf\u7ea7\u7528\u6237\uff0c\u4ea7\u751f\u7684\u6570\u636e\u91cf\u8fbe\u5343\u4ebf\u89c4\u6a21\u3002 \u56e2\u961f\u591a\u540d\u6210\u5458\u6bd5\u4e1a\u4e8e\u65b0\u52a0\u5761\u56fd\u7acb\u3001\u6e05\u534e\u3001\u5317\u5927\u7b49\u56fd\u5185\u5916\u77e5\u540d\u9ad8\u6821\uff0c\u5927\u591a\u6570\u5c0f\u4f19\u4f34\u66fe\u4efb\u804c\u4e8e\u5fae\u8f6f\u3001\u767e\u5ea6\u3001\u963f\u91cc\u7b49\u4e1a\u754c\u77e5\u540d\u516c\u53f8\u3002\u6211\u4eec\u7684\u4ef7\u503c\u89c2\u662f\u201c\u7814\u7a76\u4e1a\u754c\u4e00\u6d41\u6280\u672f\uff0c\u521b\u9020\u4ea7\u4e1a\u843d\u5730\u4ef7\u503c\u201d\u3002 [1] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748\u20138763. PMLR, 2021. [2] C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. V. Le, Y. Sung, Z. Li, and T. Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, 2021. [3] Xie C, Cai H, Song J, et al. Zero and R2D2: A Large-scale Chinese Cross-modal Benchmark and A Vision-Language Framework[J]. arXiv preprint arXiv:2205.03860, 2022.","title":"\u7ade\u8d5b\u7ec4\u7ec7"},{"location":"data/","text":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023 hosted by 360 AI Institute \u53c2\u8d5b\u6ce8\u518c \u6ce8\u518c\u94fe\u63a5\uff1a \u7528\u6237\u6ce8\u518c \u6ce8\u518c\u4fe1\u606f\u5305\u62ec\uff1a\u53c2\u8d5b\u961f\u4f0d\u540d\u79f0 \u53c2\u8d5b\u961f\u4f0d\u4ecb\u7ecd \u53c2\u8d5b\u961f\u4f0d\u6210\u5458\u4ecb\u7ecd\uff08\u59d3\u540d\uff0c\u673a\u6784\u540d\uff09 \u53c2\u8d5b\u961f\u4f0d\u8054\u7cfb\u90ae\u7bb1 \u6ce8\u518c\u6210\u529f\u540e\u4e3b\u529e\u65b9\u5c06\u5f80\u8be5\u90ae\u7bb1\u53d1\u9001\u5f53\u524d\u961f\u4f0d\u72ec\u5c5e\u7684UUID\uff0c\u7528\u4e8e\u540e\u7eed\u7ed3\u679c\u63d0\u4ea4\uff0c\u8bf7\u59a5\u5584\u4fdd\u5b58\u8be5UUID\uff0c\u5207\u52ff\u6cc4\u9732 \u6570\u636e\u4e0b\u8f7d \u53c2\u8d5b\u6ce8\u518c\u540e\uff0c\u6570\u636e\u5c06\u4ee5\u90ae\u4ef6\u7684\u5f62\u5f0f\u53d1\u9001\u94fe\u63a5\u5230\u53c2\u8d5b\u8005\u90ae\u7bb1\uff0c\u8bf7\u6ce8\u610f\u67e5\u6536 \u6570\u636e\u8bf4\u660e \u672c\u9879\u4efb\u52a1\u6db5\u76d6\u4e86\u670d\u88c5\u3001\u6570\u7801\u4ea7\u54c1\u7b49\u4f17\u591a\u5546\u54c1\u7c7b\u76ee\uff0c\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53ca\u5bf9\u5e94\u7684\u68c0\u6d4b\u6846\u6807\u6ce8\u4fe1\u606f\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u6807\u6ce8\u6570\u636e\u7684\u793a\u4f8b\u5982\u4e0b\uff1a \u521d\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_pre_contest.tgz\u548cjson_pre_contest.tgz\uff0c\u5305\u62ec\uff1a ./train\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./train.json\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u6807\u6ce8\u4fe1\u606f\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b3\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aannotations, images, categories - annotations - {'bbox': [115.640625, 187.353515625, 665.3892211914062, 462.7544860839844], 'iscrowd': 0, 'image_id': 1, 'attributes': {'occluded': False}, 'category_id': 1, 'id': 1, 'segmentation': [[115.640625,187.353515625, 781.0298461914062, 187.353515625, 781.0298461914062, 650.1080017089844, 115.640625, 650.1080017089844]], 'area': 307911.8470982518} - images - {'license': 0, 'flickr_url': '', 'coco_url': '', 'height': 800, 'id': 1, 'file_name': '1d1dd4dc32b6abdb.jpg', 'width': 800, 'date_captured': 0} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 233, 'supercategory': '', 'name': '\u5973\u58eb\u4e1d\u5dfe', 'split': 'seen'} ./test\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./test.json\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u50cf\u4fe1\u606f\u548c\u7c7b\u522b\u6c47\u603b\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b2\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aimages, categories - image - {'id': 1, 'file_name': 'a841257bff7b85b3.jpg', 'flickr_url': '', 'license': 0, 'height': 800, 'width': 800, 'date_captured': 0, 'coco_url': ''} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 466, 'supercategory': '', 'name': '\u5973\u58eb\u6cf3\u8863', 'split': 'unseen'} \u5176\u4e2d'split'\u5b57\u6bb5\u7528\u6765\u5212\u5206\u7c7b\u522b\uff0cseen\u4ee3\u8868\u8bad\u7ec3\u96c6\u51fa\u73b0\u8fc7\u7684base\u7c7b\u522b\uff0cunseen\u4ee3\u8868\u6d4b\u8bd5\u96c6\u4e2d\u7684novel\u7c7b\u522b \u51b3\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_final_contest.tgz\u548cjson_final_contest.tgz\uff0c\u6587\u4ef6\u7ec4\u7ec7\u5f62\u5f0f\u4e0e\u521d\u8d5b\u76f8\u540c\uff0c\u5728\u51b3\u8d5b\u5f00\u59cb\u540e\u53d1\u9001\u5230\u51b3\u8d5b\u53c2\u8d5b\u8005\u90ae\u7bb1 \u4f7f\u7528\u6761\u6b3e \u8be5\u6570\u636e\u53ea\u80fd\u7528\u4e8e\u975e\u5546\u4e1a\u7814\u7a76\u548c\u5b66\u672f\u6559\u80b2 \u6211\u4eec\u7981\u6b62\u7528\u6237\u5206\u53d1\u6570\u636e\u96c6\u6216\u4fee\u6539\u7248\u672c \u53c2\u8d5b\u8005\u5bf9\u4f7f\u7528\u8d5b\u9898\u6570\u636e\u627f\u62c5\u5168\u90e8\u8d23\u4efb\u3002\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u5947\u864e360\u53ca\u5176\u5173\u8054\u516c\u53f8\u3001\u6216\u5176\u8463\u4e8b\u3001\u96c7\u5458\u3001\u4ee3\u7406\u4eba\u3001\u5408\u4f5c\u4f19\u4f34\u4ee5\u53ca\u4f9b\u5e94\u5546\u5747\u5bf9\u672c\u7f51\u7ad9\u3001\u6570\u636e\u4e0d\u8d1f\u6709\u8d23\u4efb \u5728\u672c\u7f51\u7ad9\u4e0a\u6216\u901a\u8fc7\u672c\u7f51\u7ad9\u8fdb\u884c\u7684\u8bbf\u95ee\u53ca\u6240\u6709\u76f8\u5173\u6d3b\u52a8\u5747\u53d7\u4e2d\u534e\u4eba\u6c11\u5171\u548c\u56fd\u6cd5\u5f8b\u7ba1\u8f96\u5e76\u53d7\u5176\u89e3\u91ca\u3002","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"data/#open-vocabulary-detection-contest-2023","text":"hosted by 360 AI Institute","title":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023"},{"location":"data/#_1","text":"\u6ce8\u518c\u94fe\u63a5\uff1a \u7528\u6237\u6ce8\u518c \u6ce8\u518c\u4fe1\u606f\u5305\u62ec\uff1a\u53c2\u8d5b\u961f\u4f0d\u540d\u79f0 \u53c2\u8d5b\u961f\u4f0d\u4ecb\u7ecd \u53c2\u8d5b\u961f\u4f0d\u6210\u5458\u4ecb\u7ecd\uff08\u59d3\u540d\uff0c\u673a\u6784\u540d\uff09 \u53c2\u8d5b\u961f\u4f0d\u8054\u7cfb\u90ae\u7bb1 \u6ce8\u518c\u6210\u529f\u540e\u4e3b\u529e\u65b9\u5c06\u5f80\u8be5\u90ae\u7bb1\u53d1\u9001\u5f53\u524d\u961f\u4f0d\u72ec\u5c5e\u7684UUID\uff0c\u7528\u4e8e\u540e\u7eed\u7ed3\u679c\u63d0\u4ea4\uff0c\u8bf7\u59a5\u5584\u4fdd\u5b58\u8be5UUID\uff0c\u5207\u52ff\u6cc4\u9732","title":"\u53c2\u8d5b\u6ce8\u518c"},{"location":"data/#_2","text":"\u53c2\u8d5b\u6ce8\u518c\u540e\uff0c\u6570\u636e\u5c06\u4ee5\u90ae\u4ef6\u7684\u5f62\u5f0f\u53d1\u9001\u94fe\u63a5\u5230\u53c2\u8d5b\u8005\u90ae\u7bb1\uff0c\u8bf7\u6ce8\u610f\u67e5\u6536","title":"\u6570\u636e\u4e0b\u8f7d"},{"location":"data/#_3","text":"\u672c\u9879\u4efb\u52a1\u6db5\u76d6\u4e86\u670d\u88c5\u3001\u6570\u7801\u4ea7\u54c1\u7b49\u4f17\u591a\u5546\u54c1\u7c7b\u76ee\uff0c\u5bf9\u4e8e\u4e00\u4ef6\u5546\u54c1\uff0c\u6211\u4eec\u4f1a\u7ed9\u51fa\u5b83\u7684\u56fe\u7247\u4ee5\u53ca\u5bf9\u5e94\u7684\u68c0\u6d4b\u6846\u6807\u6ce8\u4fe1\u606f\u4f5c\u4e3a\u8bad\u7ec3\u6570\u636e\u3002 \u6807\u6ce8\u6570\u636e\u7684\u793a\u4f8b\u5982\u4e0b\uff1a \u521d\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_pre_contest.tgz\u548cjson_pre_contest.tgz\uff0c\u5305\u62ec\uff1a ./train\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./train.json\uff1a\u8bad\u7ec3\u96c6\u5bf9\u5e94\u7684\u6807\u6ce8\u4fe1\u606f\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b3\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aannotations, images, categories - annotations - {'bbox': [115.640625, 187.353515625, 665.3892211914062, 462.7544860839844], 'iscrowd': 0, 'image_id': 1, 'attributes': {'occluded': False}, 'category_id': 1, 'id': 1, 'segmentation': [[115.640625,187.353515625, 781.0298461914062, 187.353515625, 781.0298461914062, 650.1080017089844, 115.640625, 650.1080017089844]], 'area': 307911.8470982518} - images - {'license': 0, 'flickr_url': '', 'coco_url': '', 'height': 800, 'id': 1, 'file_name': '1d1dd4dc32b6abdb.jpg', 'width': 800, 'date_captured': 0} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 233, 'supercategory': '', 'name': '\u5973\u58eb\u4e1d\u5dfe', 'split': 'seen'} ./test\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u7247 ./test.json\uff1a\u6d4b\u8bd5\u96c6\u5bf9\u5e94\u7684\u56fe\u50cf\u4fe1\u606f\u548c\u7c7b\u522b\u6c47\u603b\uff0c\u53c2\u7167coco\u683c\u5f0f json\u6587\u4ef6\u4e2d\u5305\u542b2\u4e2a\u5b57\u6bb5\uff0c\u5206\u522b\u4e3aimages, categories - image - {'id': 1, 'file_name': 'a841257bff7b85b3.jpg', 'flickr_url': '', 'license': 0, 'height': 800, 'width': 800, 'date_captured': 0, 'coco_url': ''} - categories - {'id': 1, 'supercategory': '', 'name': '\u53e4\u8463\u6587\u73a9', 'split': 'seen'} - {'id': 2, 'supercategory': '', 'name': '\u8d1d\u96f7\u5e3d', 'split': 'seen'} - ... - {'id': 466, 'supercategory': '', 'name': '\u5973\u58eb\u6cf3\u8863', 'split': 'unseen'} \u5176\u4e2d'split'\u5b57\u6bb5\u7528\u6765\u5212\u5206\u7c7b\u522b\uff0cseen\u4ee3\u8868\u8bad\u7ec3\u96c6\u51fa\u73b0\u8fc7\u7684base\u7c7b\u522b\uff0cunseen\u4ee3\u8868\u6d4b\u8bd5\u96c6\u4e2d\u7684novel\u7c7b\u522b \u51b3\u8d5b\u6570\u636e\u96c6\u4e0b\u8f7d\u6587\u4ef6\u4e3a\uff1adata_final_contest.tgz\u548cjson_final_contest.tgz\uff0c\u6587\u4ef6\u7ec4\u7ec7\u5f62\u5f0f\u4e0e\u521d\u8d5b\u76f8\u540c\uff0c\u5728\u51b3\u8d5b\u5f00\u59cb\u540e\u53d1\u9001\u5230\u51b3\u8d5b\u53c2\u8d5b\u8005\u90ae\u7bb1","title":"\u6570\u636e\u8bf4\u660e"},{"location":"data/#_4","text":"\u8be5\u6570\u636e\u53ea\u80fd\u7528\u4e8e\u975e\u5546\u4e1a\u7814\u7a76\u548c\u5b66\u672f\u6559\u80b2 \u6211\u4eec\u7981\u6b62\u7528\u6237\u5206\u53d1\u6570\u636e\u96c6\u6216\u4fee\u6539\u7248\u672c \u53c2\u8d5b\u8005\u5bf9\u4f7f\u7528\u8d5b\u9898\u6570\u636e\u627f\u62c5\u5168\u90e8\u8d23\u4efb\u3002\u5728\u4efb\u4f55\u60c5\u51b5\u4e0b\uff0c\u5947\u864e360\u53ca\u5176\u5173\u8054\u516c\u53f8\u3001\u6216\u5176\u8463\u4e8b\u3001\u96c7\u5458\u3001\u4ee3\u7406\u4eba\u3001\u5408\u4f5c\u4f19\u4f34\u4ee5\u53ca\u4f9b\u5e94\u5546\u5747\u5bf9\u672c\u7f51\u7ad9\u3001\u6570\u636e\u4e0d\u8d1f\u6709\u8d23\u4efb \u5728\u672c\u7f51\u7ad9\u4e0a\u6216\u901a\u8fc7\u672c\u7f51\u7ad9\u8fdb\u884c\u7684\u8bbf\u95ee\u53ca\u6240\u6709\u76f8\u5173\u6d3b\u52a8\u5747\u53d7\u4e2d\u534e\u4eba\u6c11\u5171\u548c\u56fd\u6cd5\u5f8b\u7ba1\u8f96\u5e76\u53d7\u5176\u89e3\u91ca\u3002","title":"\u4f7f\u7528\u6761\u6b3e"},{"location":"leaderboard/","text":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023 hosted by 360 AI Institute Leaderboard \u6bcf\u65e5\u66f4\u65b0 \u590d\u8d5b \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd final 56.604 52.12 54.362 2 STAR OVDEA 52.28 53.176 52.728 3 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 \u518d\u7ed9KFC\u4e00\u6b21\u673a\u4f1a 50.075 54.158 52.117 4 OVD f-huge 47.885 47.146 47.516 5 wzmwzr Test1 47.635 42.649 45.142 6 \u84dd\u8272\u95ea IFEAsT30_100_P1-3++ 43.317 45.593 44.455 7 \u8102\u73af 0810_1 32.497 35.234 33.865 8 \u7b97\u6cd5\u5168\u90fd\u961f 4 21.797 40.068 30.932 9 OVD-Research 0716 15.222 31.961 23.591 10 OVD en 9.2004 29.57 19.385 \u521d\u8d5b\u6210\u7ee9 \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd bl4.16.0.8 55.392 51.131 53.262 2 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 kfc\u6c38\u5b58 47.687 52.248 49.968 3 STAR OVDC 49.359 48.541 48.95 4 wzmwzr Test1 48.501 42.679 45.59 5 lucky test_v16 40.706 49.302 45.004 6 OVD A1 40.253 46.985 43.619 7 \u84dd\u8272\u95ea B_I_F_E_A_g_50-3+ 33.984 38.129 36.057 8 \u8102\u73af 0724_1 32.828 36.77 34.799 9 \u7b97\u6cd5\u5168\u90fd\u961f \u6587\u5fc3\u4e00\u8a0014.0 33.429 35.805 34.617 10 OVD en 21.246 36.291 28.768 11 OVD-Research 0716 16.064 33.793 24.929 12 Lethe test0 12.316 35.368 23.842 13 \u5b87\u667a\u6ce2\u5bb6\u65cf G_L_F_M_di 21.232 25.345 23.288 14 mclab_415 v2 19.626 25.488 22.557 15 yahoo Test2 19.718 24.882 22.3 16 \u534e\u4e2d\u79d1\u6280\u5927\u5b66\u95ee\u53f7\u55b5\u55b5 \u55b5^3 15.436 28.36 21.898 17 testaaaaaa RegionCLIP-L1 16.224 24.967 20.596 18 XDU-Wolf vldet_test_all 10.58 26.964 18.772 19 \u6c6a\u961f\u5927\u5934\u5175 open_2 3.3975 33.835 18.616 20 OPEN-HUST openhust_v3B 16.793 18.084 17.438 21 OVD\u5c0f\u5206\u961f RCF_V1 7.3306 26.086 16.708 22 \u5f00\u653e\u4e16\u754c\u6d4b\u4e0d\u961f model_3 0.16206 33.046 16.604 23 \u5c11\u8c31 wzy_yyds 16.217 16.495 16.356 24 sysu_zhigong 2202wn 19.792 12.876 16.334 25 sysu_abcd 2201 20.035 12.465 16.25 26 temp001 22015 19.826 12.441 16.133 27 \u901a\u4e50\u961f last_1 12.968 17.778 15.373 28 \u539f\u795e\uff0c\u542f\u52a8\uff01 \u795e\u91cc\u7eeb\u534e 2.912 26.313 14.612 29 208\u961f zs208 12.236 12.979 12.608 30 \u6da7\u5cb1\u5b97\u70bc\u4e39\u95e8 Test_5 5.7197 18.968 12.344 31 \u8d5e\u7f8e\u4e07\u673a\u4e4b\u795e clip2 8.5958 9.5801 9.0879 32 \u5bf9\u5bf9\u961f test2 3.5378 2.0395 2.7887 33 HCP_OpenDetection 28 3.1386 1.6192 2.3789 34 \u9a6c\u6cfd\u5e73 Haas 1.1995 1.3711 1.2853 35 NVIDIA Yes MCGA_G1 1.5468 0.84399 1.1954 36 \u9876\u74dc\u74dc test2 1.0626 0.95454 1.0086 37 \u7237\u4eec\u8981\u6218\u6597 v2 1.0901 0.16163 0.62586 38 QAQ yo 0.0 0.13617 0.068086 39 \u54ce\uff0c\u5c31\u8fd9 v1 5.1809e-06 0.0044949 0.00225 \u7ed3\u679c\u4e0a\u4f20 \u6ce8\uff1a\u63d0\u4ea4\u6587\u4ef6\u9650\u5236\u4e3azip\u683c\u5f0f\uff0czip\u6587\u4ef6\u5185\u4ec5\u5305\u542bjson\u6587\u4ef6\uff0c\u5355\u4e2aUUID\u6bcf\u65e5\u63d0\u4ea4\u4e0a\u9650\u4e3a2\u6b21\u3002\u7528\u6237\u4e0a\u4f20\u540e\uff0c\u6b21\u65e5\u4f1a\u4ee5\u90ae\u4ef6\u5f62\u5f0f\u544a\u77e5\u7ed3\u679c\u3002 json\u6587\u4ef6\u5185\u4e3b\u4f53\u4e3a\u4e00\u4e2aList\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5b50\u9879\u5e94\u8be5\u4e3aDict,\u5177\u4f53\u4e3a\u4ee5\u4e0b\u683c\u5f0f\uff1a {'image_id': 19805, 'category_id': 1, 'bbox': [276.3232421875, 123.5966796875, 240.05747985839844, 602.2131958007812], 'score': 0.99} \u4e0a\u4f20\u7ed3\u679c \u7ade\u8d5b\u52a8\u6001 news \u57fa\u4e8e\u53c2\u8d5b\u961f\u4f0d\u6570\u76ee\u3001\u961f\u4f0d\u63d0\u4ea4\u9891\u7387\u3001\u521d\u8d5b\u6210\u7ee9\u8003\u91cf\u4e0e\u6bd4\u8d5b\u5956\u91d1\u7684\u8bbe\u7f6e\uff0c\u8fdb\u5165\u590d\u8d5b\u961f\u4f0d\u4e3a\u521d\u8d5b\u6210\u7ee9\u7684\u524d12\u652f\uff0c\u5373\u53c2\u4e0e\u590d\u8d5b\u961f\u4f0d\u6570\u91cf\u4e3a\u53ef\u83b7\u5956\u961f\u4f0d\u6570\u91cf\u7684200%\u3002\u8bf7\u6ce8\u610f\uff0c\u590d\u8d5b\u5c06\u4e8e8\u670810\u65e5\u51c6\u65f6\u5f00\u542f\uff0c\u6301\u7eed\u65f6\u95f4\u4e3a20\u5929\uff0c\u5c06\u4e8e8\u670830\u65e524\u70b9\u5173\u95ed\u63d0\u4ea4\u7aef\u53e3\uff01 \u8bf7\u6ce8\u610f\uff01\u6211\u4eec\u5df2\u5c06\u521d\u8d5b\u6ce8\u518c\u65f6\u95f4\u4e0e\u6bd4\u8d5b\u65f6\u95f4\u65f6\u95f4\u622a\u6b62\u65e5\u671f\u5ef6\u957f\u4e00\u4e2a\u6708\uff0c\u5c06\u539f\u5148\u76846\u670830\u65e5\u63a8\u540e\u81f37\u670830\u65e5\u3002 \u793e\u533a\u8ba8\u8bba\u548c\u7b54\u7591 \u5982\u6709\u6bd4\u8d5b\u76f8\u5173\u95ee\u9898\uff0c\u53ef\u5728 Issues \u8fdb\u884c\u63d0\u95ee\u8ba8\u8bba","title":"Leaderboard"},{"location":"leaderboard/#open-vocabulary-detection-contest-2023","text":"hosted by 360 AI Institute","title":"Open Vocabulary Detection Contest - \u5f00\u653e\u4e16\u754c\u76ee\u6807\u68c0\u6d4b\u7ade\u8d5b 2023"},{"location":"leaderboard/#leaderboard","text":"\u6bcf\u65e5\u66f4\u65b0 \u590d\u8d5b \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd final 56.604 52.12 54.362 2 STAR OVDEA 52.28 53.176 52.728 3 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 \u518d\u7ed9KFC\u4e00\u6b21\u673a\u4f1a 50.075 54.158 52.117 4 OVD f-huge 47.885 47.146 47.516 5 wzmwzr Test1 47.635 42.649 45.142 6 \u84dd\u8272\u95ea IFEAsT30_100_P1-3++ 43.317 45.593 44.455 7 \u8102\u73af 0810_1 32.497 35.234 33.865 8 \u7b97\u6cd5\u5168\u90fd\u961f 4 21.797 40.068 30.932 9 OVD-Research 0716 15.222 31.961 23.591 10 OVD en 9.2004 29.57 19.385 \u521d\u8d5b\u6210\u7ee9 \u6392\u884c \u7ec4\u7ec7 \u6a21\u578b novel base all 1 \u5434\u601d\u6cfd bl4.16.0.8 55.392 51.131 53.262 2 \u54b1\u4eec\u7ec4\u6709\u540d\u79f0\u5417 kfc\u6c38\u5b58 47.687 52.248 49.968 3 STAR OVDC 49.359 48.541 48.95 4 wzmwzr Test1 48.501 42.679 45.59 5 lucky test_v16 40.706 49.302 45.004 6 OVD A1 40.253 46.985 43.619 7 \u84dd\u8272\u95ea B_I_F_E_A_g_50-3+ 33.984 38.129 36.057 8 \u8102\u73af 0724_1 32.828 36.77 34.799 9 \u7b97\u6cd5\u5168\u90fd\u961f \u6587\u5fc3\u4e00\u8a0014.0 33.429 35.805 34.617 10 OVD en 21.246 36.291 28.768 11 OVD-Research 0716 16.064 33.793 24.929 12 Lethe test0 12.316 35.368 23.842 13 \u5b87\u667a\u6ce2\u5bb6\u65cf G_L_F_M_di 21.232 25.345 23.288 14 mclab_415 v2 19.626 25.488 22.557 15 yahoo Test2 19.718 24.882 22.3 16 \u534e\u4e2d\u79d1\u6280\u5927\u5b66\u95ee\u53f7\u55b5\u55b5 \u55b5^3 15.436 28.36 21.898 17 testaaaaaa RegionCLIP-L1 16.224 24.967 20.596 18 XDU-Wolf vldet_test_all 10.58 26.964 18.772 19 \u6c6a\u961f\u5927\u5934\u5175 open_2 3.3975 33.835 18.616 20 OPEN-HUST openhust_v3B 16.793 18.084 17.438 21 OVD\u5c0f\u5206\u961f RCF_V1 7.3306 26.086 16.708 22 \u5f00\u653e\u4e16\u754c\u6d4b\u4e0d\u961f model_3 0.16206 33.046 16.604 23 \u5c11\u8c31 wzy_yyds 16.217 16.495 16.356 24 sysu_zhigong 2202wn 19.792 12.876 16.334 25 sysu_abcd 2201 20.035 12.465 16.25 26 temp001 22015 19.826 12.441 16.133 27 \u901a\u4e50\u961f last_1 12.968 17.778 15.373 28 \u539f\u795e\uff0c\u542f\u52a8\uff01 \u795e\u91cc\u7eeb\u534e 2.912 26.313 14.612 29 208\u961f zs208 12.236 12.979 12.608 30 \u6da7\u5cb1\u5b97\u70bc\u4e39\u95e8 Test_5 5.7197 18.968 12.344 31 \u8d5e\u7f8e\u4e07\u673a\u4e4b\u795e clip2 8.5958 9.5801 9.0879 32 \u5bf9\u5bf9\u961f test2 3.5378 2.0395 2.7887 33 HCP_OpenDetection 28 3.1386 1.6192 2.3789 34 \u9a6c\u6cfd\u5e73 Haas 1.1995 1.3711 1.2853 35 NVIDIA Yes MCGA_G1 1.5468 0.84399 1.1954 36 \u9876\u74dc\u74dc test2 1.0626 0.95454 1.0086 37 \u7237\u4eec\u8981\u6218\u6597 v2 1.0901 0.16163 0.62586 38 QAQ yo 0.0 0.13617 0.068086 39 \u54ce\uff0c\u5c31\u8fd9 v1 5.1809e-06 0.0044949 0.00225","title":"Leaderboard"},{"location":"leaderboard/#_1","text":"\u6ce8\uff1a\u63d0\u4ea4\u6587\u4ef6\u9650\u5236\u4e3azip\u683c\u5f0f\uff0czip\u6587\u4ef6\u5185\u4ec5\u5305\u542bjson\u6587\u4ef6\uff0c\u5355\u4e2aUUID\u6bcf\u65e5\u63d0\u4ea4\u4e0a\u9650\u4e3a2\u6b21\u3002\u7528\u6237\u4e0a\u4f20\u540e\uff0c\u6b21\u65e5\u4f1a\u4ee5\u90ae\u4ef6\u5f62\u5f0f\u544a\u77e5\u7ed3\u679c\u3002 json\u6587\u4ef6\u5185\u4e3b\u4f53\u4e3a\u4e00\u4e2aList\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5b50\u9879\u5e94\u8be5\u4e3aDict,\u5177\u4f53\u4e3a\u4ee5\u4e0b\u683c\u5f0f\uff1a {'image_id': 19805, 'category_id': 1, 'bbox': [276.3232421875, 123.5966796875, 240.05747985839844, 602.2131958007812], 'score': 0.99} \u4e0a\u4f20\u7ed3\u679c","title":"\u7ed3\u679c\u4e0a\u4f20"},{"location":"leaderboard/#_2","text":"news \u57fa\u4e8e\u53c2\u8d5b\u961f\u4f0d\u6570\u76ee\u3001\u961f\u4f0d\u63d0\u4ea4\u9891\u7387\u3001\u521d\u8d5b\u6210\u7ee9\u8003\u91cf\u4e0e\u6bd4\u8d5b\u5956\u91d1\u7684\u8bbe\u7f6e\uff0c\u8fdb\u5165\u590d\u8d5b\u961f\u4f0d\u4e3a\u521d\u8d5b\u6210\u7ee9\u7684\u524d12\u652f\uff0c\u5373\u53c2\u4e0e\u590d\u8d5b\u961f\u4f0d\u6570\u91cf\u4e3a\u53ef\u83b7\u5956\u961f\u4f0d\u6570\u91cf\u7684200%\u3002\u8bf7\u6ce8\u610f\uff0c\u590d\u8d5b\u5c06\u4e8e8\u670810\u65e5\u51c6\u65f6\u5f00\u542f\uff0c\u6301\u7eed\u65f6\u95f4\u4e3a20\u5929\uff0c\u5c06\u4e8e8\u670830\u65e524\u70b9\u5173\u95ed\u63d0\u4ea4\u7aef\u53e3\uff01 \u8bf7\u6ce8\u610f\uff01\u6211\u4eec\u5df2\u5c06\u521d\u8d5b\u6ce8\u518c\u65f6\u95f4\u4e0e\u6bd4\u8d5b\u65f6\u95f4\u65f6\u95f4\u622a\u6b62\u65e5\u671f\u5ef6\u957f\u4e00\u4e2a\u6708\uff0c\u5c06\u539f\u5148\u76846\u670830\u65e5\u63a8\u540e\u81f37\u670830\u65e5\u3002","title":"\u7ade\u8d5b\u52a8\u6001"},{"location":"leaderboard/#_3","text":"\u5982\u6709\u6bd4\u8d5b\u76f8\u5173\u95ee\u9898\uff0c\u53ef\u5728 Issues \u8fdb\u884c\u63d0\u95ee\u8ba8\u8bba","title":"\u793e\u533a\u8ba8\u8bba\u548c\u7b54\u7591"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index c3a2999..d5d50cb 100755
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,17 +2,17 @@
None
- 2023-09-15
+ 2023-11-19
daily
None
- 2023-09-15
+ 2023-11-19
daily
None
- 2023-09-15
+ 2023-11-19
daily
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 889f117..4be7ea5 100755
Binary files a/sitemap.xml.gz and b/sitemap.xml.gz differ