diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/CreateEvalModel.mel b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/CreateEvalModel.mel similarity index 100% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/CreateEvalModel.mel rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/CreateEvalModel.mel diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ImageNet1K_intensity.xml b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ImageNet1K_intensity.xml similarity index 100% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ImageNet1K_intensity.xml rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ImageNet1K_intensity.xml diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ImageNet1K_mean.xml b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ImageNet1K_mean.xml similarity index 100% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ImageNet1K_mean.xml rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ImageNet1K_mean.xml diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/Macros.ndl b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/Macros.ndl similarity index 100% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/Macros.ndl rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/Macros.ndl diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/README.md b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/README.md similarity index 53% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/README.md rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/README.md index ec3a75f25..4df9df462 100644 --- a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/README.md +++ b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/README.md @@ -9,15 +9,12 @@ |Training |Stochastic gradient descent with momentum. ## Details -The network configurations and experiment settings in this this folder resemble the ones in the original [ResNet paper](http://arxiv.org/abs/1512.03385) with few minor changes inspired by [this work](https://github.com/facebook/fb.resnet.torch). -The following table contains results as well as links to pre-trained models that can be used in various applications. +The network configurations and experiment settings in this this folder resemble the ones in the original [ResNet paper](http://arxiv.org/abs/1512.03385) strictly without any extra optimization. +The following table contains results. | Network | Top-1 error | Top-5 error | Model | ------------- | ----------- | ----------- | ---------- -| ResNet-18 | 29.57 | 10.41 | [Download](https://www.cntk.ai/resnet/ResNet_18.model) -| ResNet-34 | 27.31 | 8.97 | [Download](https://www.cntk.ai/resnet/ResNet_34.model) -| ResNet-50 | 24.74 | 7.56 | [Download](https://www.cntk.ai/resnet/ResNet_50.model) -| ResNet-152 | 22.57 | 6.44 | [Download](https://www.cntk.ai/resnet/ResNet_152.model) +| ResNet-50 | 24.58 | 7.43 | ## Notes This work is an implementation of ResNets in CNTK. If you are interested in the original implementation of ResNet, follow [this link](https://github.com/KaimingHe/deep-residual-networks). \ No newline at end of file diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ResNet_50.cntk b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ResNet_50.cntk similarity index 62% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ResNet_50.cntk rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ResNet_50.cntk index f5f8a7cee..7fa0b0ead 100644 --- a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ResNet_50.cntk +++ b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ResNet_50.cntk @@ -56,10 +56,6 @@ Train=[ reader=[ readerType="ImageReader" - # Map file which maps images to labels using the following format: - # - # Example: - # C:\Data\ImageNet\2012\train\n01440764\n01440764_10026.JPEG0 file="$DataDir$/train_map.txt" # Randomize images before every epoch. Possible values: None, Auto. Default: Auto. randomize="Auto" @@ -78,22 +74,6 @@ Train=[ # Crop scale ratio jitter type. # Possible values: None, UniRatio, UniLength, UniArea. Default: UniRatio jitterType="UniRatio" - # Interpolation to use when scaling image to width x height size. - # Possible values: nearest, linear, cubic, lanczos. Default: linear. - #interpolations="cubic" - # Aspect ratio jitter radius. Default is 0 (disabled). - #aspectRatioRadius=0:0.2 - # Brightness, contrast and color jittering. Default is 0 (disabled). - # Using 0 in the first epoch so the network can process original images. - #brightnessRadius=0:0.2 - #contrastRadius=0:0.2 - #saturationRadius=0:0.4 - # Intensity jittering: enabled if file is specified and intensityStdDev > 0. - # The file stores 1x3 vector (eigenvalues) and 3x3 matrix (eigenvectors) in OpenCV XML format. - #intensityFile="$ConfigDir$/ImageNet1K_intensity.xml" - # StdDev for intensity jittering. Start from the second epoch. - #intensityStdDev=0:0.1 - # Mean subtraction: enabled if file is specified. # The file stores mean values for each pixel in OpenCV matrix XML format. meanFile="$ConfigDir$/ImageNet1K_mean.xml" ] @@ -107,15 +87,11 @@ PBN=[ action="pbn" modelPath="$ModelDir$/ResNet_50" # Set minibatch size for testing. - minibatchSize=32 + minibatchSize=256 iters=30 reader=[ readerType="ImageReader" - # Map file which maps images to labels using the following format: - # - # Example: - # C:\Data\ImageNet\2012\train\n01440764\n01440764_10026.JPEG0 file="$DataDir$/train_map.txt" # Randomize images before every epoch. Possible values: None, Auto. Default: Auto. randomize="Auto" @@ -134,22 +110,6 @@ PBN=[ # Crop scale ratio jitter type. # Possible values: None, UniRatio, UniLength, UniArea. Default: UniRatio jitterType="UniRatio" - # Interpolation to use when scaling image to width x height size. - # Possible values: nearest, linear, cubic, lanczos. Default: linear. - #interpolations="cubic" - # Aspect ratio jitter radius. Default is 0 (disabled). - #aspectRatioRadius=0:0.2 - # Brightness, contrast and color jittering. Default is 0 (disabled). - # Using 0 in the first epoch so the network can process original images. - #brightnessRadius=0:0.2 - #contrastRadius=0:0.2 - #saturationRadius=0:0.4 - # Intensity jittering: enabled if file is specified and intensityStdDev > 0. - # The file stores 1x3 vector (eigenvalues) and 3x3 matrix (eigenvectors) in OpenCV XML format. - #intensityFile="$ConfigDir$/ImageNet1K_intensity.xml" - # StdDev for intensity jittering. Start from the second epoch. - #intensityStdDev=0:0.1 - # Mean subtraction: enabled if file is specified. # The file stores mean values for each pixel in OpenCV matrix XML format. meanFile="$ConfigDir$/ImageNet1K_mean.xml" ] diff --git a/Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ResNet_50.ndl b/Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ResNet_50.ndl similarity index 100% rename from Examples/Image/Miscellaneous/ImageNet/ResNetHandsOn/ResNet_50.ndl rename to Examples/Image/Miscellaneous/ImageNet/ResNetOriginal/ResNet_50.ndl