Details

Autor(en) / Beteiligte

• Shao, Shuai
• Li, Zeming
• Zhang, Tianyuan
• Peng, Chao
• Yu, Gang
• Zhang, Xiangyu
• Li, Jing
• Sun, Jian

Titel

Objects365: A Large-Scale, High-Quality Dataset for Object Detection

Ist Teil von

• 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, p.8429-8438

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Quelle

IEEE Xplore

Beschreibungen/Notizen

• In this paper, we introduce a new large-scale object detection dataset, Objects365, which has 365 object categories over 600K training images. More than 10 million, high-quality bounding boxes are manually labeled through a three-step, carefully designed annotation pipeline. It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community. Objects365 can serve as a better feature learning dataset for localization-sensitive tasks like object detection and semantic segmentation. The Objects365 pre-trained models significantly outperform ImageNet pre-trained models with 5.6 points gain (42 vs 36.4) based on the standard setting of 90K iterations on COCO benchmark. Even compared with much long training time like 540K iterations, our Objects365 pretrained model with 90K iterations still have 2.7 points gain (42 vs 39.3). Meanwhile, the finetuning time can be greatly reduced (up to 10 times) when reaching the same accuracy. Better generalization ability of Object365 has also been verified on CityPersons, VOC segmentation, and ADE tasks. The dataset as well as the pretrained-models have been released at www.objects365.org.