Lower top1/top5 accuracy on ImageNet for reference model

[zimmerrol]

Issue summary

I tried to reproduce the accuracy values (top1 and top5) for the reference ImageNet model (CaffeNet). However, I only get a top5 accuracy of ~67% which is lower than the reported 80% here.

Steps to reproduce

I used the model definition and weights from GitHub. I evaluated the model using this simple script. I used the pre-processing (transforming from RGB to BGR, image resizing, center cropping, and ImageNet mean subtraction) indicated in the ImageNet examples of Caffe.

I uploaded the predicted classes and the ground-truth labels as a python list here, in case someone wants to take a look at them,

Tried solutions

I redownloaded the network weights and verified the preprocessing.

System configuration

• Operating system: see below
• Compiler:
• CUDA version (if applicable):
• CUDNN version (if applicable):
• BLAS:
• Python version (if using pycaffe):
• MATLAB version (if using matcaffe):

I used the latest gpu docker container.

Issue checklist

• [x] read the guidelines and removed the first paragraph
• [x] written a short summary and detailed steps to reproduce
• [x] explained how solutions to related problems failed (tick if found none)
• [x] filled system configuration
• [x] attached relevant logs/config files (tick if not applicable)