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Added credits for training bvlc models
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@ -18,6 +18,8 @@ The best validation performance during training was iteration 358,000 with valid
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This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop.
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This model obtains a top-1 accuracy 57.1% and a top-5 accuracy 80.2% on the validation set, using just the center crop.
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(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
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(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)
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This model was trained by Evan Shelhamer @shelhamer
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## License
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## License
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The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
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The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
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@ -5,6 +5,7 @@ caffemodel_url: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel
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license: non-commercial
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license: non-commercial
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sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
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sha1: 405fc5acd08a3bb12de8ee5e23a96bec22f08204
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caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
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caffe_commit: bc614d1bd91896e3faceaf40b23b72dab47d44f5
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gist_id: 866e2aa1fd707b89b913
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---
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---
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This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
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This model is a replication of the model described in the [GoogleNet](http://arxiv.org/abs/1409.4842) publication. We would like to thank Christian Szegedy for all his help in the replication of GoogleNet model.
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@ -25,6 +26,7 @@ Timings for bvlc_googlenet with cuDNN using batch_size:128 on a K40c:
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- Average Backward pass: 1123.84 ms.
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- Average Backward pass: 1123.84 ms.
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- Average Forward-Backward: 1688.8 ms.
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- Average Forward-Backward: 1688.8 ms.
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This model was trained by Sergio Guadarrama @sguada
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## License
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## License
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@ -18,6 +18,8 @@ The best validation performance during training was iteration 313,000 with valid
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This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.
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This model obtains a top-1 accuracy 57.4% and a top-5 accuracy 80.4% on the validation set, using just the center crop.
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(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)
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(Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)
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This model was trained by Jeff Donahue @jeffdonahue
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## License
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## License
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The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
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The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
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@ -13,6 +13,8 @@ Try the [detection example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/m
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*N.B. For research purposes, make use of the official R-CNN package and not this example.*
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*N.B. For research purposes, make use of the official R-CNN package and not this example.*
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This model was trained by Ross Girshick @rbgirshick
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## License
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## License
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The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
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The data used to train this model comes from the ImageNet project, which distributes its database to researchers who agree to a following term of access:
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@ -15,6 +15,8 @@ The final performance:
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I1017 07:36:17.370730 31333 solver.cpp:247] Iteration 100000, Testing net (#0)
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I1017 07:36:17.370730 31333 solver.cpp:247] Iteration 100000, Testing net (#0)
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I1017 07:36:34.248730 31333 solver.cpp:298] Test net output #0: accuracy = 0.3916
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I1017 07:36:34.248730 31333 solver.cpp:298] Test net output #0: accuracy = 0.3916
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This model was trained by Sergey Karayev @sergeyk
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## License
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## License
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The Flickr Style dataset contains only URLs to images.
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The Flickr Style dataset contains only URLs to images.
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