caffe学习--cifar10学习-ubuntu16.04-gtx650tiboost--1g--03--20171103

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：