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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 <cdluminate@gmail.com> with the help of txt2man for Debian
according to program's help message.
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