Provided by: caffe-tools-cpu_1.0.0-8ubuntu1_amd64
caffe - command line brew for Caffe
caffe <COMMAND> <FLAGS>
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.
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
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
This manpage is written by Zhou Mo <firstname.lastname@example.org> with the help of txt2man for Debian according to program's help message. 10 August 2016 CAFFE(1)