mmclassification/configs/resnet

Deep Residual Learning for Image Recognition

Introduction

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

Results and models

Cifar10

Model	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Config	Download
ResNet-18-b16x8	11.17	0.56	94.82		config	model | log
ResNet-34-b16x8	21.28	1.16	95.34		config	model | log
ResNet-50-b16x8	23.52	1.31	95.55		config	model | log
ResNet-101-b16x8	42.51	2.52	95.58		config	model | log
ResNet-152-b16x8	58.16	3.74	95.76		config	model | log

Cifar100

Model	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Config	Download
ResNet-50-b16x8	23.71	1.31	79.9	95.19	config	model | log

ImageNet

Model	Params(M)	Flops(G)	Top-1 (%)	Top-5 (%)	Config	Download
ResNet-18	11.69	1.82	70.07	89.44	config	model | log
ResNet-34	21.8	3.68	73.85	91.53	config	model | log
ResNet-50	25.56	4.12	76.55	93.15	config	model | log
ResNet-101	44.55	7.85	78.18	94.03	config	model | log
ResNet-152	60.19	11.58	78.63	94.16	config	model | log
ResNetV1D-50	25.58	4.36	77.54	93.57	config	model | log
ResNetV1D-101	44.57	8.09	78.93	94.48	config	model | log
ResNetV1D-152	60.21	11.82	79.41	94.7	config	model | log