classification ./examples/cifar10/cifar10_full.prototxt ./examples/cifar10/cifar10_full_iter_70000.caffemodel.h5 ./examples/cifar10/mean.binaryproto ./examples/cifar10/labels.txt ~/Downloads/images/horse/8.jpg sea@sea-X550JK:~/caffe$ classification --help Usage: classification deploy.prototxt network.caffemodel mean.binaryproto labels.txt img.jpg classification models/bvlc_reference_caffenet/deploy.prototxt models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel models/bvlc_reference_caffenet/mean.binaryproto models/bvlc_reference_caffenet/labels.txt ~/Downloads/images/horse/8.jpg
用cifar10训练的结果进行分类：
python python/classify.py --model_def examples/cifar10/cifar10_quick.prototxt --pretrained_model examples/cifar10/cifar10_quick_iter_5000.caffemodel.h5 --center_only examples/images/cat.jpg foo
python python/classify.py --model_def models/bvlc_reference_caffenet/deploy.prototxt --pretrained_model models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel --center_only examples/images/cat.jpg foo
I1103 16:59:58.189568 25346 net.cpp:200] conv1 does not need backward computation. I1103 16:59:58.189571 25346 net.cpp:200] data does not need backward computation. I1103 16:59:58.189574 25346 net.cpp:242] This network produces output prob I1103 16:59:58.189584 25346 net.cpp:255] Network initialization done. I1103 16:59:58.303480 25346 upgrade_proto.cpp:44] Attempting to upgrade input file specified using deprecated transformation parameters: models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel I1103 16:59:58.303509 25346 upgrade_proto.cpp:47] Successfully upgraded file specified using deprecated data transformation parameters. W1103 16:59:58.303514 25346 upgrade_proto.cpp:49] Note that future Caffe releases will only support transform_param messages for transformation fields. I1103 16:59:58.303517 25346 upgrade_proto.cpp:53] Attempting to upgrade input file specified using deprecated V1LayerParameter: models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel I1103 16:59:58.504439 25346 upgrade_proto.cpp:61] Successfully upgraded file specified using deprecated V1LayerParameter I1103 16:59:58.559579 25346 net.cpp:744] Ignoring source layer loss /usr/local/lib/python2.7/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15. warn("The default mode, 'constant', will be changed to 'reflect' in " Loading file: examples/images/cat.jpg Classifying 1 inputs. Done in 1.20 s. Predictions : [[ 7.96905475e-09 2.68402800e-05 4.61699550e-08 5.81401345e-08 3.00355154e-08 1.08543240e-07 7.21305184e-08 6.65618529e-07 4.10124194e-05 8.26508540e-07 2.64434061e-06 4.29981719e-06 2.29038033e-05 9.16178294e-07 2.02221463e-06 1.91530648e-06 8.36403979e-06 5.25011237e-05 1.32120860e-07 7.34086640e-08 7.26202700e-07 6.55063502e-07 2.83661024e-07 8.35531750e-08 1.45248293e-07 3.21299929e-08 5.94506417e-08 1.11880944e-07 2.61020752e-08 1.33058847e-05 2.00340565e-07 7.72992621e-08 2.47393245e-07 5.60683127e-08 7.26820346e-08 2.93914972e-08 8.09441403e-08 1.17543671e-07 1.24727379e-07 1.14408145e-07
sea@sea-X550JK:~/caffe$ python readFromFooAndShow.py sz = 4112 nl.shape = (1, 1000) ssdict = [(281, 0.30427486), (285, 0.1783575), (282, 0.16652611), (287, 0.15713461), (278, 0.042343788), (277, 0.039970074),
(283, 0.011617188), (876, 0.0085467361), (284, 0.0076080137), (463, 0.0066294265), (904, 0.0065242196), (968, 0.0063064895),
(259, 0.0051229554), (330, 0.0046631121), (760, 0.0044421358), (478, 0.0042510382), (331, 0.0039331503), (728, 0.003812969),
(280, 0.0035846629), (588, 0.0033092475), (861, 0.0028945252), (332, 0.0026644215), (333, 0.0022166823), (151, 0.0021597522),
(356, 0.0018406865), (552, 0.0016959301), (435, 0.00094394217), (896, 0.00084631733), (937, 0.00082845741), (335, 0.00076790486),
(897, 0.0007364807), (519, 0.00072649814), (674, 0.00063642312), (457, 0.00062823156), (263, 0.00055513595), (969, 0.00043508445),
(773, 0.00041424474), (794, 0.00039454823), (230, 0.00037321725), (534, 0.00036081325), (104, 0.00032497221), (272, 0.00032023937),
(473, 0.0003057541), (725, 0.00030245754), (742, 0.00029926837), (722, 0.00028606801), (987, 0.00024712173), (622, 0.00024177019),
(274, 0.00023734267),
下面是分类的过程bvlc_reference_caffenet：
模型bvlc_reference_caffenet 是用于分类的：
- BAIR Reference CaffeNet in
models/bvlc_reference_caffenet: AlexNet trained on ILSVRC 2012, with a minor variation from the version as described in ImageNet classification with deep convolutional neural networks by Krizhevsky et al. in NIPS 2012. (Trained by Jeff Donahue @jeffdonahue)
./build/examples/cpp_classification/classification.bin models/bvlc_reference_caffenet/deploy.prototxt models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel data/ilsvrc12/imagenet_mean.binaryproto data/ilsvrc12/synset_words.txt examples/images/cat.jpg
sea@sea-X550JK:~/caffe$ ./build/examples/cpp_classification/classification.bin > models/bvlc_reference_caffenet/deploy.prototxt > models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel > data/ilsvrc12/imagenet_mean.binaryproto > data/ilsvrc12/synset_words.txt > examples/images/cat.jpg ---------- Prediction for examples/images/cat.jpg ---------- 0.3134 - "n02123045 tabby, tabby cat" 0.2380 - "n02123159 tiger cat" 0.1235 - "n02124075 Egyptian cat" 0.1003 - "n02119022 red fox, Vulpes vulpes" 0.0715 - "n02127052 lynx, catamount"
预测的实例/图像/————————cat.jpg
“n02123045 46 6猫，虎斑猫”
“n02123159 0.2380老虎猫”
“n02124075 0.1235埃及猫”
“n02119022 0.1003赤狐，狐狐”
“n02127052猞猁，0.0715美洲豹”
./build/examples/cpp_classification/classification.bin models/bvlc_reference_caffenet/deploy.prototxt models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel data/ilsvrc12/imagenet_mean.binaryproto data/ilsvrc12/synset_words.txt /home/sea/Downloads/images/person.jpeg
/home/sea/Downloads/images/person.jpeg
---------- Prediction for /home/sea/Downloads/images/person.jpeg ---------- 0.8322 - "n04350905 suit, suit of clothes" 0.0799 - "n04591157 Windsor tie" 0.0588 - "n02883205 bow tie, bow-tie, bowtie" 0.0051 - "n10148035 groom, bridegroom" 0.0041 - "n02865351 bolo tie, bolo, bola tie, bola"
“n04350905 0.8322服，服之衣”
“n04591157 0.0799领带。”
“n02883205 0.0588蝴蝶结领带，领结，bowtie”
“n10148035马夫，bridegroom率”
“n02865351联络0.0041蛋糕，蛋糕，球铁，球”
识别装修图片：
./build/examples/cpp_classification/classification.bin models/bvlc_reference_caffenet/deploy.prototxt models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel data/ilsvrc12/imagenet_mean.binaryproto data/ilsvrc12/synset_words.txt /home/sea/Downloads/images/a.jpg
> /home/sea/Downloads/images/a.jpg ---------- Prediction for /home/sea/Downloads/images/a.jpg ---------- 0.3274 - "n04081281 restaurant, eating house, eating place, eatery" 0.1335 - "n03761084 microwave, microwave oven" 0.1196 - "n03661043 library" 0.0768 - "n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin" 0.0710 - "n03742115 medicine chest, medicine cabinet"
0.3274“n04081281餐厅，吃房子，吃的地方，餐馆” 0.1335“n03761084微波，微波炉” 0.1196“n03661043图书馆” 0.0768“n04553703洗脸盆，洗手盆，洗脸盆，洗手盆，洗手盆” 0.0710“n03742115药箱，药箱”
目标检测、定位的+目标识别的fetch_faster_rcnn_models：
https://github.com/rbgirshick/py-faster-rcnn/blob/master/data/scripts/fetch_faster_rcnn_models.sh
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Download pre-computed Faster R-CNN detectors cd $FRCN_ROOT
./data/scripts/fetch_faster_rcnn_models.sh This will populate the $FRCN_ROOT/data folder with faster_rcnn_models. See data/README.md for details. These models were trained on VOC 2007 trainval.
ref https://github.com/rbgirshick/py-faster-rcnn/blob/master/data/scripts/fetch_faster_rcnn_models.sh
目标检测--resnet-50：
./build/examples/cpp_classification/classification.bin /media/sea/wsWin10/wsWindows10/ws_caffe/model-zoo/ResNet-50/deploy.prototxt /media/sea/wsWin10/wsWindows10/ws_caffe/model-zoo/ResNet-50/ResNet-50-model.caffemodel data/ilsvrc12/imagenet_mean.binaryproto data/ilsvrc12/synset_words.txt /home/sea/Downloads/images/a.jpg
人脸识别的：