Caffe-Caffe Models

1.bvlc_reference_caffenet.caffemodel

下载地址：http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodel
来源：caffe中ImageNet
tutorial（Brew ImagesnNet）训练结果
Paper：ImageNet Classification with Deep Convolutional Neural Networks（NIPS2012  Alex Krizhevsky   Ilya Sutskever   Geoffrey E. Hinton）

This model is the result of following the Caffe ImageNet
model training instructions. It is a replication of the model described in the AlexNet publication
with some differences:

not training with the relighting data-augmentation;
the order of pooling and normalization layers is switched (in CaffeNet, pooling is done before normalization).

This model is snapshot of iteration 310,000. The best validation performance during training was iteration 313,000 with validation accuracy 57.412% and loss 1.82328. 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. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy still.)

This model was trained by Jeff Donahue @jeffdonahue

AlexNet：



deploy.prototxt:



train_val.prototxt:

2.LeNet

3.finetune_flickr_style.caffemodel

Target：
        Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data.

Info：
        Fine-tuning takes an already learned model, adapts the architecture, and resumes training from the already learned model weights. Let’s fine-tune the BVLC-distributed CaffeNet
model on a different dataset, Flickr Style, to predict image style instead of object category.
结构图如下：

deploy.prototxt



train_val.prototxt