Shortcuts

ResNet

Deep Residual Learning for Image Recognition

Abstract

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. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.

The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. 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.

Results and models

The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don’t have evaluation results.

Model resolution Params(M) Flops(G) Download
ResNet-50-mill 224x224 86.74 15.14 model

The “mill” means using the mutil-label pretrain weight from ImageNet-21K Pretraining for the Masses.

Cifar10

Model Params(M) Flops(G) Top-1 (%) Top-5 (%) Config Download
ResNet-18 11.17 0.56 94.82 99.87 config model | log
ResNet-34 21.28 1.16 95.34 99.87 config model | log
ResNet-50 23.52 1.31 95.55 99.91 config model | log
ResNet-101 42.51 2.52 95.58 99.87 config model | log
ResNet-152 58.16 3.74 95.76 99.89 config model | log

Cifar100

Model Params(M) Flops(G) Top-1 (%) Top-5 (%) Config Download
ResNet-50 23.71 1.31 79.90 95.19 config model | log

ImageNet-1k

Model Params(M) Flops(G) Top-1 (%) Top-5 (%) Config Download
ResNet-18 11.69 1.82 69.90 89.43 config model | log
ResNet-34 21.8 3.68 73.62 91.59 config model | log
ResNet-50 25.56 4.12 76.55 93.06 config model | log
ResNet-101 44.55 7.85 77.97 94.06 config model | log
ResNet-152 60.19 11.58 78.48 94.13 config model | log
ResNetV1C-50 25.58 4.36 77.01 93.58 config model | log
ResNetV1C-101 44.57 8.09 78.30 94.27 config model | log
ResNetV1C-152 60.21 11.82 78.76 94.41 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 94.70 config model | log
ResNet-50 (fp16) 25.56 4.12 76.30 93.07 config model | log
Wide-ResNet-50* 68.88 11.44 78.48 94.08 config model
Wide-ResNet-101* 126.89 22.81 78.84 94.28 config model
ResNet-50 (rsb-a1) 25.56 4.12 80.12 94.78 config model | log
ResNet-50 (rsb-a2) 25.56 4.12 79.55 94.37 config model | log
ResNet-50 (rsb-a3) 25.56 4.12 78.30 93.80 config model | log

The “rsb” means using the training settings from ResNet strikes back: An improved training procedure in timm.

Models with * are converted from the official repo. The config files of these models are only for validation. We don’t ensure these config files’ training accuracy and welcome you to contribute your reproduction results.

CUB-200-2011

Model Pretrain resolution Params(M) Flops(G) Top-1 (%) Config Download
ResNet-50 ImageNet-21k-mill 448x448 23.92 16.48 88.45 config model | log

Citation

@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}
}
Read the Docs v: latest
Versions
master
latest
stable
Downloads
html
epub
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.