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Data Structure ?
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README.md
2019年4月16日の智源学者計画始動暨共同研究室発表会で、Megvii Technologyは汎用物体検出データセットObjects365を発表した。このデータセットには63万枚の画像が含まれており、ImageNetやCOCOなどのデータセットをはるかに上回っている。
Megvii Technologyの最高科学者である孫剣氏は、Megvii研究院が最近発表した汎用物体検出データセットObjects365について紹介した。このデータセットには合計63万枚の画像が含まれ、365のカテゴリを網羅し、1000万に達するボックス数を持ち、Pascal VOC、ImageNet、COCOデータセットを上回っている。
このデータセットには、人、衣類、居室、浴室、キッチン、オフィス、電化製品、交通、食べ物、果物、野菜、動物、スポーツ、楽器の14の大カテゴリが含まれ、それぞれのカテゴリには数十の小カテゴリがある。
COCOとVOCデータセットに収録されている80カテゴリと20カテゴリのみを考慮した場合でも、画像1枚あたりの平均ボックス数とカテゴリ数の2つの指標で、Objects365は依然としてCOCOとVOCよりも優れている。また、アノテーションの過程で見落としが減少し、平均アノテーション領域の割合もCOCOとVOCを上回っている。
さらに、画像1枚あたりのカテゴリ網羅性に関しては、これまでのデータセットと比較して、Objects365はより広範なカテゴリを網羅している。
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