用caffe訓練自己的資料集(三)
阿新 • • 發佈:2018-11-06
本文主要參考了:https://blog.csdn.net/heimu24/article/details/53581362
https://blog.csdn.net/gaohuazhao/article/details/69568267
六、使用訓練好的模型
前兩篇部落格已經把模型訓練好了,本次就是使用已經訓練好的模型引數識別圖片。
首先在myfile4資料夾下新建images資料夾,把想要檢測的圖片放入資料夾中,可以用下載的淘寶圖片測試。
1、在myfile4資料夾中新建deploy.prototxt檔案,內容如下:
name: "myfile4" layer { name: "data" type: "Input" top: "data" input_param{shape:{dim:1 dim:3 dim:32 dim:32}} } layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 32 pad:2 kernel_size: 5 stride: 1 } } layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name:"relu1" type:"ReLU" bottom:"pool1" top:"pool1" } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 32 pad:2 kernel_size: 5 stride: 1 } } layer { name:"relu2" type:"ReLU" bottom:"conv2" top:"conv2" } layer { name: "pool2" type: "Pooling" bottom: "conv2" top: "pool2" pooling_param { pool: AVE kernel_size: 3 stride: 2 } } layer { name:"conv3" type:"Convolution" bottom:"pool2" top:"conv3" param{ lr_mult:1 } param{ lr_mult:2 } convolution_param { num_output:64 pad:2 kernel_size:5 stride:1 } } layer { name:"relu3" type:"ReLU" bottom:"conv3" top:"conv3" } layer { name:"pool3" type:"Pooling" bottom:"conv3" top:"pool3" pooling_param { pool:AVE kernel_size:3 stride:2 } } layer { name: "ip1" type: "InnerProduct" bottom: "pool3" top: "ip1" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 64 } } layer { name: "ip2" type: "InnerProduct" bottom: "ip1" top: "ip2" param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 10 } } layer { name: "prob" type: "Softmax" bottom: "ip2" top: "prob" }
2、在myfile4資料夾中新建檔案synset_work.txt,內容如下:
biao fajia kuzi xiangzi yizi dianshi suannai xiangshui hufupin xiezi
3、在myfile4資料夾下新建demo.sh。內容如下:
./build/examples/cpp_classification/classification.bin examples/myfile4/deploy.prototxt examples/myfile4/my_iter_2000.caffemodel examples/myfile4/mean.binaryproto examples/myfile4/synset_words.txt examples/myfile4/images/222.jpg
4、在caffe目錄下執行examples/myfile4/demo.sh
完成後就會出現識別的結果。自此便完成了自己資料集的訓練與識別。