The run_se_benchmarks.m script will evaluate each of these models on the ImageNet validation set. It will download the models automatically if you have not already done so (note that these evaluations require a copy of the imagenet data). The results of the evaluations are given below - note there are minor differences to the original scores (listed under official) due to variations in preprocessing (full details of the evaluation can be found here):
model
top-1 error (offical)
top-5 error (official)
SE-ResNet-50-mcn
22.30 (22.37)
6.30 (6.36)
SE-ResNet-101-mcn
21.59 (21.75)
5.81 (5.72)
SE-ResNet-152-mcn
21.38 (21.34)
5.60 (5.54)
SE-BN-Inception-mcn
24.16 (23.62)
7.35 (7.04)
SE-ResNeXt-50-32x4d-mcn
21.01 (20.97)
5.58 (5.54)
SE-ResNeXt-101-32x4d-mcn
19.73 (19.81)
4.98 (4.96)
SENet-mcn
18.67 (18.68)
4.50 (4.47)
There may be some difference in how the Inception network should be preprocessed relative to the others (this model exhibits a noticeable degradation). To give some idea of the relative computational burdens of each model, esimates are provided below:
Each estimate corresponds to computing a single element batch. This table was generated with convnet-burden - the repo has a list of the assumptions used produce estimations. Clicking on the model name should give a more detailed breakdown.
The ordering of the imagenet labels differs from the standard ordering commonly found in caffe, pytorch etc. These are remapped automically in the evaluation code. The mapping between the synsets indices can be found here.