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deploy.prototxt
quick_solver.prototxt
readme.md
solver.prototxt
train_val.prototxt

readme.md

name caffemodel caffemodel_url license sha1 caffe_commit
BVLC GoogleNet Model
bvlc_googlenet.caffemodel
unrestricted
405fc5acd08a3bb12de8ee5e23a96bec22f08204
bc614d1bd91896e3faceaf40b23b72dab47d44f5

This model is a replication of the model described in the GoogleNet publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.

Differences:

  • not training with the relighting data-augmentation;
  • not training with the scale or aspect-ratio data-augmentation;
  • uses "xavier" to initialize the weights instead of "gaussian";
  • quick_solver.prototxt uses a different learning rate decay policy than the original solver.prototxt, that allows a much faster training (60 epochs vs 250 epochs);

The bundled model is the iteration 2,400,000 snapshot (60 epochs) using quick_solver.prototxt

This bundled model obtains a top-1 accuracy 68.7% (31.3% error) and a top-5 accuracy 88.9% (11.1% error) on the validation set, using just the center crop. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)

Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:

  • Average Forward pass: 562.841 ms.
  • Average Backward pass: 1123.84 ms.
  • Average Forward-Backward: 1688.8 ms.

This model was trained by Sergio Guadarrama @sguada

License

This model is released for unrestricted use.

Sample validation output

      I0712 11:53:19.627121 109762 caffe.cpp:292] loss1/loss1 = 1.86667 (* 0.3 = 0.560002 loss)
      I0712 11:53:19.627127 109762 caffe.cpp:292] loss1/top-1 = 0.55522
      I0712 11:53:19.627131 109762 caffe.cpp:292] loss1/top-5 = 0.804981
      I0712 11:53:19.627136 109762 caffe.cpp:292] loss2/loss1 = 1.50183 (* 0.3 = 0.45055 loss)
      I0712 11:53:19.627140 109762 caffe.cpp:292] loss2/top-1 = 0.629759
      I0712 11:53:19.627142 109762 caffe.cpp:292] loss2/top-5 = 0.856821
      I0712 11:53:19.627147 109762 caffe.cpp:292] loss3/loss3 = 1.25635 (* 1 = 1.25635 loss)
      I0712 11:53:19.627153 109762 caffe.cpp:292] loss3/top-1 = 0.689299
      I0712 11:53:19.627156 109762 caffe.cpp:292] loss3/top-5 = 0.891441
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