Caffe2 - Caffemodel 转换为 Caffe2 pb 模型 1. 单输入单输出 - caffe_translator.py Caffe2 提供了将 caffemodel 转换为 caffe2 模型的工具——caffe_translator.py. 其使用: python -m caffe2.python.caffe_translator deploy.prototxt pretrained.caffemodel 即得到 caffe2 的模型:init_net.pb parser.add_argument("caffemodel", help="Caffe trained model.") = args.caffemodel output_init_net = args.init_net output_predict_net = args.predict_net
人体姿态模型下载路径: BODY25: http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel COCO: http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel MPI : http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel 具体完整代码为: = os.path.join( modelpath, "pose/mpi/pose_iter_160000.caffemodel") = os.path.join( modelpath, "pose/coco/pose_iter_440000.caffemodel")
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/build/tools/caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10 3、使用已有模型提取特征(caffemodel /build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples softmax的输出了,所以提取fc7层 4.)lmdb:输出的数据格式是lmdb,还可以是leveldb ---- Test测试(用cmdcaffe命令行)(train_test.prototxt + caffemodel image_test/train_val.prototxt -weights examples/image_test/caffenet_train/caffenet_train_iter_10000.caffemodel Test分类单个输入图像 利用训练好的模型,预测结果,5个输入参数 (train_val.prototxt + caffemodel + mean.binaryproto + synset_words.txt
Net<float> > testnet; std::string modelproto = jstring2string(env, modelProto); std::string caffemodel = jstring2string(env, caffeModel); __android_log_print(ANDROID_LOG_INFO, "XOR", "modelproto:%s, caffemodel :%s", modelproto.c_str(), caffemodel.c_str()); testnet.reset(new Net<float>(modelproto, TEST)); testnet->CopyTrainedLayersFrom(caffemodel); // obtain the input MemoryData layer and pass the input ); } 代码中假设模型文件位于sdcard中,所以在运行代码之前,需要将model.prototxt和XOR_iter_5000000.caffemodel文件push到/sdcard/中。
build/tools/caffe time -model examples/mnist/lenet_train_test.prototxt -iterations 10 3、使用已有模型提取特征(caffemodel /build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples -------------------------------------------------------- Test测试(用cmdcaffe命令行)(train_test.prototxt + caffemodel image_test/train_val.prototxt -weights examples/image_test/caffenet_train/caffenet_train_iter_10000.caffemodel Test分类单个输入图像 利用训练好的模型,预测结果,5个输入参数(train_val.prototxt + caffemodel + mean.binaryproto + synset_words.txt
sofa', 'train', 'tvmonitor') NETS = {'vgg16': ('VGG16', 'VGG16_faster_rcnn_final.caffemodel '), 'zf': ('ZF', 'ZF_faster_rcnn_final.caffemodel')} #增加ax参数 def vis_detections format(caffemodel)) if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu caffe.set_device(args.gpu_id) cfg.GPU_ID = args.gpu_id net = caffe.Net(prototxt, caffemodel , caffe.TEST) print '\n\nLoaded network {:s}'.format(caffemodel) # Warmup on a dummy image
test \ > -model examples/mnist/lenet_train_test.prototxt \ > -weights examples/mnist/lenet_iter_10000.caffemodel examples/mnist/lenet_train_test.prototxt \ //指定模型描述文本文件 > -weights examples/mnist/lenet_iter_10000.caffemodel (1) CLASSES =(‘background‘, ‘xxx’)(这里是你的类别名) (2) NETS ={‘vgg16’: (‘VGG16’, ‘VGG16_faster_rcnn_final.caffemodel ’), ‘zf’: (‘ZF’,’ZF_faster_rcnn_final.caffemodel’)} (这里是你训练完成的model) (3) 在训练集图片中找一张出来放入py-faster-rcnn
模型下载 BODY_25 - pose_iter_584000.caffemodel COCO - pose_iter_440000.caffemodel MPI - pose_iter_160000. caffemodel Face - pose_iter_116000.caffemodel Hand - pose_iter_102000.caffemodel 2.
NETS = {'vgg16': ('VGG16', 'VGG16_faster_rcnn_final.caffemodel'), 'zf': (' ZF', 'ZF_faster_rcnn_final.caffemodel')} def vis_detections(im, class_name, dets, format(caffemodel)) if args.cpu_mode: caffe.set_mode_cpu() else: caffe.set_mode_gpu caffe.set_device(args.gpu_id) cfg.GPU_ID = args.gpu_id net = caffe.Net(prototxt, caffemodel , caffe.TEST) print '\n\nLoaded network {:s}'.format(caffemodel) im_path = '/data/images/'
使用OpenCV的DNN模块以及Caffe模型,必须要有.prototxt和.caffemodel两种文件。 但face_detector文件夹中,只有.prototxt一类文件,即缺少训练好的.caffemodel。. prototxt和.caffemodel的作用如下: The .prototxt file(s) which define the model architecture (i.e., the layers themselves) The .caffemodel file which contains the weights for the actual layers face_detector文件分析: :超参数文件 test.prototxt:测试网络文件 train.prototxt:训练网络文件 本教程直接使用训练好的.caffemodel来进行人脸检测,即只需要.caffemodel和deploy.prototxt
支持目前大多数主流深度学习框架的模型,如下所示: ONNX(.onnx,.pb) Keras(.h5,.keras) CoreML(.mlmodel) TensorFlow Lite(.tflite) Netron对Caffe(.caffemodel 这里举两个示例:Caffe的(.caffemodel)和TensorFlow(.pb,.meta) 下面是VGG-16.caffemodel的部分可视化截图 ? Netron has experimental support for Caffe (.caffemodel), Caffe2 (predict_net.pb), MXNet (.model, -symbol.json
/BVLC/caffe/tree/master/models/bvlc_alexnet 预训练模型地址:http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel file-readme-md VGG16的预训练模型: http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel file-readme-md VGG19的预训练模型:http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel BVLC/caffe/tree/master/models/bvlc_googlenet 预训练模型地址:http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel github.com/pertusa/InceptionBN-21K-for-Caffe 预训练模型地址:http://www.dlsi.ua.es/~pertusa/deep/Inception21k.caffemodel
实现对图像常见分类,OpenCV3.3的DNN模块使用的模型支持1000种常见图像分类、googlenet深度学习网络模型是2014图像分类比赛的冠军、首先是下载相关的数据模型文件 bvlc_googlenet.caffemodel 模型文件需要从以下地址下载即可: http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel 二:编程实现 首先我们需要加载它官方指定的一张测试图像 \n"); return -1; } 然后我们需要声明模型数据的路径与标记数据路径,加载创建网络模型,代码实现如下: // create googlenet with caffemodel << std::endl; std::cerr << "prototxt: " << modelTxt << std::endl; std::cerr << "caffemodel \n"); return -1; } // create googlenet with caffemodel text and bin Net net
1、下载caffemodel 本例中我们使用的是pascalcontext-fcn32的模型,这个下载链接在它的文件夹里有,就是url那个文件 下载 pascalcontext-fcn32s-heavy.caffemodel transpose((2,0,1)) # load net net = caffe.Net('voc-fcn8s/deploy.prototxt', 'voc-fcn8s/fcn8s-heavy-pascal.caffemodel ', caffe.TEST) //这里是我们需要用到的网络模型和caffemodel,也要改 # shape for input (data blob is N x C x H x W), set data /pascalcontext-fcn32s-heavy.caffemodel', caffe.TEST) //然后也是把deploy和caffemodel准备好了 # shape for input
/build/release/ios/build/libmdl-static.a to your iOS project 把 caffemodel 转换成 mdl 格式 #Convert model.prototxt and model.caffemodel to model.min.json and data.min.bin that mdl use . /build/release/x86/tools/build # copy your model.prototxt and model.caffemodel to this path # also need /caffe2mdl model.prototxt model.caffemodel data # after this command, model.min.json data.min.bin will
利用数据集外的图片测试 这项测试的前提条件是,拥有属于自己的caffemodel。 在caffe根目录/examples/MobileNet-SSD下运行 python merge_bn.py 即可生成指定名称的caffemodel。 ' #should be your snapshot caffemodel deploy_proto ='/home/wluo/DeepLearning/CaffeLearning-weiliu89/ ' #指定生成的caffemodel名称 caffemodel生成之后,可以在caffe根目录/examples/MobileNet-SSD/执行 python demo.py 开始测试,在运行demo.py ' #caffemodel名称 test_dir = "images_power" #测试图片文件夹名 CLASSES修改为自己数据集的类别和background。
/build/release/ios/build/libmdl-static.a to your iOS project 把caffemodel转换成mdl格式 #Convert model.prototxt and model.caffemodel to model.min.json and data.min.bin that mdl use . /build/release/x86/tools/build # copy your model.prototxt and model.caffemodel to this path # also need /caffe2mdl model.prototxt model.caffemodel data # after this command, model.min.json data.min.bin will
age_net.caffemodel:用于年龄检测的预训练模型权重。 deploy_age.prototxt:年龄检测模型的模型架构。 res10_300x300_ssd_iter_140000_fp16.caffemodel:用于人脸检测的预训练模型权重。 deploy.prototxt.txt:人脸检测模型的模型架构。 prototxt 文件提供了年龄和性别的网络配置,而 .caffemodel 文件定义了图层参数的内部状态。 然后,对于人脸、年龄和性别检测模型,定义权重和结构变量。 opencv_face_detector_uint8.pb" ageProto = "/content/age_deploy.prototxt" ageModel = "/content/age_net.caffemodel " genderProto = "/content/gender_deploy.prototxt" genderModel = "/content/gender_net.caffemodel" 第 4
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