Deep Residual Learning for Image Recognition

CVPR 2016 Kaiming HeXiangyu ZhangShaoqing RenJian Sun

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Image Classification CIFAR-10 ResNet Percentage correct 93.57 # 24
Image Classification CIFAR-10 ResNet Percentage error 6.43 # 13
Object Detection COCO Faster R-CNN + box refinement + context + multi-scale testing Bounding Box AP 34.9 # 29
Object Detection PASCAL VOC 2007 ResNet-101 MAP 76.4% # 10