Deep Residual Learning for Image Recognition

CVPR 2016 Kaiming He • Xiangyu Zhang • Shaoqing Ren • Jian Sun

We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We also present analysis on CIFAR-10 with 100 and 1000 layers. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Image Classification CIFAR-10 ResNet Percentage correct 93.57 # 19
Image Classification CIFAR-10 ResNet Percentage error 6.43 # 19
Object Detection COCO Faster R-CNN + box refinement + context + multi-scale testing Bounding Box AP 34.9 # 18
Object Detection PASCAL VOC 2007 ResNet-101 MAP 76.4% # 10