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Trained ResNet Torch models

These are ResNet models trainined on ImageNet. The accuracy on the ImageNet validation set are included below.

The ResNet-50 model has a batch normalization layer after the addition, instead of immediately after the convolution layer. The ResNet-200 model is the full pre-activation variant from "Identity Mappings in Deep Residual Networks".

ImageNet 1-crop error rates (224x224)
Network Top-1 error Top-5 error
ResNet-18 30.43 10.76
ResNet-34 26.73 8.74
ResNet-50 24.01 7.02
ResNet-101 22.44 6.21
ResNet-152 22.16 6.16
ResNet-200 21.66 1 5.79
ImageNet 10-crop error rates
Network Top-1 error Top-5 error
ResNet-18 28.22 9.42
ResNet-34 24.76 7.35
ResNet-50 22.24 6.08
ResNet-101 21.08 5.35
ResNet-152 20.69 5.21
ResNet-200 20.15 4.93
ImageNet charts

See the convergence plots for charts of training and validation error and training loss after every epoch.

Fine-tuning on a custom dataset

Your images don't need to be pre-processed or packaged in a database, but you need to arrange them so that your dataset contains a train and a val directory, which each contain sub-directories for every label. For example:


You can then use the included ImageNet data loader with your dataset and train with the -resetClassifer and -nClasses options:

th main.lua -retrain resnet-50.t7 -data [path-to-directory-with-train-and-val] -resetClassifier true -nClasses 80

The labels will be sorted alphabetically. The first output of the network corresponds to the label that comes first alphabetically.

You can find how to create custom data loader in datasets readme.


To get the top 5 predicted of a model for a given input image, you can use the classify.lua script. For example:

th classify.lua resnet-101.t7 img1.jpg img2.jpg ...

Example output:

Classes for     cat.jpg
0.77302575111389        Egyptian cat
0.060410376638174       tabby, tabby cat 
0.040622022002935       tiger cat
0.025837801396847       lynx, catamount
0.018691379576921       window screen

Extracting image features

The extract-features.lua script will extract the image features from an image and save them as a serialized Torch tensor. To use it, first download one of the trained models above. Next run it using

th extract-features.lua resnet-101.t7 img1.jpg img2.jpg ...

This will save a file called features.t7 in the current directory. You can then load the image features in Torch.

local features = torch.load('features.t7')


1 This is on a test crop of size 224x224. On a test crop of size 320x320, the error rate is 20.1/4.8.