https://blog.csdn.net/defi_wang/article/details/107450428
https://blog.csdn.net/defi_wang/article/details/107889818 https://blog.csdn.net/defi_wang/article/details/108032208
https://blog.csdn.net/defi_wang/article/details/107936757
VGGNet [options] command [arg...]
| command | description |
|---|---|
| state | Show the VGG net state |
| train | train the network |
| verify | verify the pre-trained network with the test-set |
| classify | classify an input picture with the pre-trained network |
| option | description |
|---|---|
| -v | Verbose mode to output more logs |
| -y | Proceed the operation without any prompt |
VGGNet state [--bn/-batchnorm] [-n numclass] [-s/--smallsize] [train_output]
If no arg is specified, it will print the VGG-D net at default.
examples:
VGGNet.exe state --bn --numclass 10 --smallsize
print the neutral network state with batchnorm layers, the output number of classes and use the 32x32 small input image instead the 224x224 image.
VGGNet.exe I:\catdog.pt
print the information of neutral network loading from I:\catdog.pt.
| name | shortname | arg | description |
|---|---|---|---|
| batchnorm bn |
n/a | n/a | enable batchnorm after CNN |
| numclass | n | num of classes | The specified final number of classes, the default value is 1000 |
| smallsize | s | n/a | Use 32x32 input instead of the original 224*224 |
VGGNet train image_set_root_path train_output [-b/--batchsize batchsize] [-e/--epochnum epochnum] [-l/--learningrate fixed_learningrate] [--bn/--batchnorm] [-n numclass] [-s/--smallsize] [--showloss once_num_batch] [--clean]
| name | shortname | arg | description |
|---|---|---|---|
| batchsize | b | batchsize | the batch size of sending to network |
| epochnum | e | epochnum | the number of train epochs |
| learningrate | l | learning rate | the fixed learning rate (*)if it is not specified, default learning rate is used, dynamic learning rate is used |
| batchnorm bn |
n/a | n/a | enable batchnorm after CNN |
| numclass | n | num of classes | The specified final number of classes, the default value is 1000 |
| smallsize | s | n/a | Use 32x32 input instead of the original 224*224 |
| showloss | n/a | once_num_batch | stat. the loss every num batch |
| clean | n/a | n/a | clean the previous pre-trained net state file |
Train a network with the specified train image set, and the image set folder structure is like as
{image_set_root_path}
|-training_set
| |-tag1
| | |-- images......
| |-tag2
| | |-- images......
| ......
|_test_set
|-tag1
| |-- images......
Examples
VGGNet.exe train I:\CatDog I:\catdog.pt --bn -b 64 -l 0.0001 --showloss 10
Train the image set lies at I:\CatDog, and save the output to I:\catdog.pt, the batchnorm layers will be introduced, and the batch size is 64, the learning rate use the fixed 0.0001, and show the loss rate every 10 batches.
VGGNet verify image_set_root_path pretrain_network Verify the test-set and show the pass-rate and other information
VGGNet verify I:\CatsDogs I:\catdog.pt
VGGNet classify pretrain_network image_file_path With the specified pre-trained network, classify a image.
VGGNet classify I:\catdog.pt PIC_001.png