pretrained

Trained ResNet Torch models

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

• ResNet-18
• ResNet-34
• ResNet-50
• ResNet-101
• ResNet-152
• ResNet-200

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:

train/<label1>/<image.jpg>
train/<label2>/<image.jpg>
val/<label1>/<image.jpg>
val/<label2>/<image.jpg>

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.

Classification

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')

Notes

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.