模型models，包括：图像分类、性别判断、年龄估算

--- 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](http://arxiv.org/abs/1409.4842) 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.