It’s just a mean computed from a random subset of imagenet pictures. See here for a mean/std computation that was done in torch.

  
      github.com
  
  
    soumith/imagenet-multiGPU.torch/blob/master/donkey.lua#L154 18

   split = 0,
   verbose = true,
   forceClasses = trainLoader.classes -- force consistent class indices between trainLoader and testLoader
}
torch.save(testCache, testLoader)
testLoader.sampleHookTest = testHook
end
collectgarbage()
-- End of test loader section


-- Estimate the per-channel mean/std (so that the loaders can normalize appropriately)
if paths.filep(meanstdCache) then
local meanstd = torch.load(meanstdCache)
mean = meanstd.mean
std = meanstd.std
print('Loaded mean and std from cache.')
else
local tm = torch.Timer()
local nSamples = 10000
print('Estimating the mean (per-channel, shared for all pixels) over ' .. nSamples .. ' randomly sampled training images')
local meanEstimate = {0,0,0}



  
  
    
    
  
  


They just kept the magic numbers without keeping the tedious mean/std computation part when porting to pytorch.