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.
|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|