caffe(1) · carta

Usage

caffe <COMMAND> <FLAGS>

Overview

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

Releases

Unchanged in 3 releases since Debian 9 (Stretch).

9–11