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package:caffemodel2-bvlc-googlenet (v1.0.0)
License: unrestricted (see readme.me)
Creation date: 2017-05-14
Source: GitHub
cID: 1dc07ee0f4742028:1d4b2f6fff794929

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Description  

This meta package is our attempt to provide a unified Python API, CLI and JSON meta description for different package managers and building tools to automatically download and install different components (models, data sets, libraries, frameworks, tools) necessary to run portable program pipelines across evolving platforms. Our on-going project is to make the onboarding process as simple as possible via this platform. Please check this CK white paper and don't hesitate to contact us if you have suggestions or feedback!

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ReadMe  


name: BVLC GoogleNet Model caffemodel: bvlc_googlenet.caffemodel caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel license: unrestricted sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204 caffe_commit: 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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