NAME
caffe - command line brew for Caffe
SYNOPSIS
caffe <COMMAND> <FLAGS>
DESCRIPTION
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.
COMMANDS
- train
-
train or finetune a model
- test
-
score a model
- device_query
-
show GPU diagnostic information
- time
-
benchmark model execution time
FREQUENTLY USED FLAGS
- -gpu
-
(Optional; run in GPU mode on given device IDs separated by ','. Use '-gpu all' to run on all available GPUs. The effective training batch size is multiplied by the number of devices.) type: string default: ""
- -iterations
-
(The number of iterations to run.) type: int32 default: 50
- -level
-
(Optional; network level.) type: int32 default: 0
- -model
-
(The model definition protocol buffer text file..) type: string default: ""
- -phase
-
(Optional; network phase (TRAIN or TEST). Only used for 'time'.) type: string default: ""
- -sighup_effect
-
(Optional; action to take when a SIGHUP signal is received: snapshot, stop or none.) type: string default: "snapshot"
- -sigint_effect
-
(Optional; action to take when a SIGINT signal is received: snapshot, stop or none.) type: string default: "stop"
- -snapshot
-
(Optional; the snapshot solver state to resume training.) type: string default: ""
- -solver
-
(The solver definition protocol buffer text file.) type: string default: ""
- -stage
-
(Optional; network stages (not to be confused with phase), separated by ','.) type: string default: ""
- -weights
-
(Optional; the pretrained weights to initialize finetuning, separated by ','. Cannot be set simultaneously with snapshot.) type: string default: ""
- -help
-
Show complete help messages.
OTHER CAFFE UTILITIES
Apart from the "caffe" command line utility, there are also some utilities available, run them with "-h" or "--help" argument to see corresponding help.
- •
-
convert_imageset
- •
-
convert_cifar_data
- •
-
compute_image_mean
- •
-
convert_mnist_siamese_data
- •
-
upgrade_net_proto_binary
- •
-
extract_features
- •
-
upgrade_solver_proto_text
- •
-
classification
- •
-
upgrade_net_proto_text
- •
-
convert_mnist_data
EXAMPLES
Train a new Network
$ caffe train -solver solver.prototxt
Resume training a network from a snapshot
$ caffe train -solver solver.prototxt -snapshot bvlc_alexnet.solverstate
Fine-tune a network
$ caffe train -solver solver.prototxt -weights pre_trained.caffemodel
Test (evaluate) a trained model for 100 iterations, on GPU 0
$ caffe test -model train_val.prototxt -weights bvlc_alexnet.caffemodel -gpu 0 -iterations 100
Run a benchmark against AlexNet on GPU 0
$ caffe time -model deploy.prototxt -gpu 0
Check CUDA device availability of GPU 0
$ caffe device_query -gpu 0
HOMEPAGE
http://caffe.berkeleyvision.org
BUGS
https://github.com/BVLC/caffe/issues
AUTHOR
This manpage is written by Zhou Mo <[email protected]> with the help of txt2man for Debian according to program's help message.