By Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.

Microsoft Research Asia (MSRA).

0. Introduction
1. Disclaimer and Known Issues
2. Results
3. Third-party Re-implementations

This repository contains the original models (ResNet-50, ResNet-101, and ResNet-152) described in the paper "Deep Residual Learning for Image Recognition" (http://arxiv.org/abs/1512.03385). These models are those used in [ILSVRC] (http://image-net.org/challenges/LSVRC/2015/) and COCO 2015 competitions, which won the 1st places in: ImageNet classification, ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

If you use these models in your research, please cite:

@article{He2015,
	author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	title = {Deep Residual Learning for Image Recognition},
	journal = {arXiv preprint arXiv:1512.03385},
	year = {2015}
}

0. These models are converted from our own implementation to a recent version of Caffe. There might be numerical differences.
1. These models are for the usage of testing or fine-tuning.
2. These models were not trained using this version of Caffe.
3. If you want to train these models using this version of Caffe without modifications, please notice that:
   • GPU memory might be insufficient for extremely deep models.
   • Implementation of data augmentation might be different (see our paper about the data augmentation we used).
   • Changes of mini-batch size should impact accuracy (we use a mini-batch of 256 images on 8 GPUs, that is, 32 images per GPU).
4. In our BN layers, the provided mean and variance are strictly computed using average (not moving average) on a sufficiently large training batch after the training procedure. Using moving average might lead to different results.
5. In the BN paper, the BN layer learns gamma/beta. To implement BN in this version of Caffe, we use its provided "batch_norm_layer" (which has no gamma/beta learned) followed by "scale_layer" (which learns gamma/beta).
6. We use Caffe's implementation of SGD: W := momentum*W + lr*g. If you want to port these models to other libraries (e.g., Torch), please pay careful attention to the possibly different implementation of SGD: W := momentum*W + (1-momentum)*lr*g, which changes the effective learning rates.

0. 1-crop validation error on ImageNet (center 224x224 crop from resized image with shorter side=256):

   model	top-1	top-5
   VGG-16	28.5%	9.9%
   ResNet-50	24.7%	7.8%
   ResNet-101	23.6%	7.1%
   ResNet-152	23.0%	6.7%

1. 10-crop validation error on ImageNet (averaging softmax scores of 10 224x224 crops from resized image with shorter side=256), the same as those in the paper:

   model	top-1	top-5
   ResNet-50	22.9%	6.7%
   ResNet-101	21.8%	6.1%
   ResNet-152	21.4%	5.7%

Deep residual networks are very easy to implement and train. We recommend to see also the following third-party re-implementations and extensions:

0. Torch, CIFAR-10, with ResNet-20 to ResNet-110: https://github.com/gcr/torch-residual-networks
1. Lasagne, CIFAR-10, with ResNet-32 and ResNet-56: https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning