Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. The depth of representations is of central importance for many visual recognition tasks. We also present analysis on CIFAR-10 with 1 layers. and more Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. This result won the 1st place on the ILSVRC 2015 classification task. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers-8× deeper than VGG nets but still having lower complexity. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. Visualizations of network structures (tools from ethereon):Ĭurves on ImageNet (solid lines: 1-crop val error dashed lines: training error):ġ-crop validation error on ImageNet (center 224x224 crop from resized image with shorter side=256): modelġ0-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: modelĭeep residual networks are very easy to implement and train.Deeper neural networks are more difficult to train. If you want to port these models to other libraries (e.g., Torch, CNTK), please pay careful attention to the possibly different implementation of SGD with momentum: v := momentum*v + (1-momentum)*lr*g, which changes the effective learning rates.
In the BN paper, the BN layer learns gamma/beta.Using moving average might lead to different results. The numerical results are very stable (variation of val error < 0.1%). 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.There might be some other untested issues.We randomly shuffle data at the beginning of every epoch.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).GPU memory might be insufficient for extremely deep models. Paper overview: Deep Residual Learning for Image Recognition 1.If you want to train these models using this version of Caffe without modifications, please notice that:.These models were not trained using this version of Caffe.These models are for the usage of testing or fine-tuning.We present a residual learning framework to ease. Deeper neural networks are more difficult to train. Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp.
Deep residual learning for image recognition code#
The numerical results using this code are as in the tables below. Deep Residual Learning for Image Recognition. These models are converted from our own implementation to a recent version of Caffe (, b590f1d).
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