Caffe_Windows学习笔记（七）细粒度图像分类

0、参考文献

[1]https://github.com/ouceduxzk/Fine_Grained_Classification/tree/master/codes

[2]http://blog.csdn.net/lynnandwei/article/details/44458033

1.

install caffe

download bvlc_googlenet.caffemodel

 http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel

download images into the cars_train and cars_test folder under the prepare folder 

 http://ai.stanford.edu/~jkrause/cars/car_dataset.html

2.

in the prepare folder, change the directory of train_path and test_path to your own path 

run : python preprocessing.py, which will generate the  crop_train and crop_test

train_path = 'D:/caffe_test/caffe-master/fine_grained/cars_train/'
train_files = os.listdir(train_path)
train_files = [ f for f in train_files if f.endswith('jpg')]
test_path = 'D:/caffe_test/caffe-master/fine_grained/cars_test/'
test_files = os.listdir(test_path)

遇到unindent的报错的话，用Notepad解决，具体参考https://www.crifan.com/python_syntax_error_indentationerror/

结果：

3.

sh pre.sh , which will call caffe to save the images into a database

#!/bin/bash
D:/caffe_test/caffe-master/Build/x64/Release/convert_imageset D:/caffe_test/caffe-master/fine-grained/crop_train/ train2.txt ./train -resize_width=227 -resize_height=227 -check_size -shuffle true
D:/caffe_test/caffe-master/Build/x64/Release/convert_imageset D:/caffe_test/caffe-master/fine-grained/crop_test/ test2.txt ./test -resize_width=227 -resize_height=227 -check_size -shuffle true

结果：

4.

change the path in the models/finetuen_car/train_val.prototxt into the path of your db

layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
data_param {
source: "D:/caffe_test/caffe-master/fine-grained/train/"
batch_size: 32
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
crop_size: 224
mean_value: 104
mean_value: 117
mean_value: 123
}
data_param {
source: "D:/caffe_test/caffe-master/fine-grained/test/"
batch_size: 50
backend: LMDB
}
}

5.

sh run.sh, the models will be saved in the models directory

solver.prototxt:

net: "D:/caffe_test/caffe-master/fine-grained/train_val.prototxt"
test_iter: 100
test_interval: 400
test_initialization: false
display: 20
base_lr: 0.001
lr_policy: "step"
stepsize: 10000
gamma: 0.1
max_iter: 50000
momentum: 0.9
weight_decay: 0.0005
snapshot: 1000
snapshot_prefix: "D:/caffe_test/caffe-master/fine-grained/car_googlenet"
solver_mode: GPU

run.sh:

#!/bin/bash
D:/caffe_test/caffe-master/Build/x64/Release/caffe train --solver D:/caffe_test/caffe-master/fine-grained/solver.prototxt --weights D:/caffe_test/caffe-master/fine-grained/bvlc_googlenet.caffemodel --gpu 0

结果：



一直遇到*** Check failure stack trace: ***的问题，但是路径都没错啊，目前没解决。

不知道是不是Windows不好跑的原因，我放到Linux上就通过了，汗……

6.

hyper parameters are in solver.prototxt file and you can play with that .