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Merge pull request #142 from shelhamer/data-aux
Sort out models, data, examples, and tools
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‎.gitignore‎

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## General
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# Compiled Object files
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*.slo
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*.lo
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*.pb.cc
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*_pb2.py
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# bin files
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# Compiled python
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*.pyc
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# Compiled MATLAB
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*.mex
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*.mexa64
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*.mexmaci64
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# build, distribute, and bins
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build/*
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distribute/*
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*.testbin
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*.bin
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# vim swp files
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# Editor temporaries
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*.swp
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# matlab binary
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*.mexa64
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*~
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# IPython notebook checkpoints
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.ipynb_checkpoints
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# anything under data/ unless we force include them
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data/*
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# anything under distribute
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distribute/*
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## Caffe
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# user's specified config
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# User's build configuration
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Makefile.config
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docs/_site
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# Models, Data, and Examples are either
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# 1. reference, and not casually committed
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# 2. custom, and live on their own unless they're deliberated contributed
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models/*
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data/*
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examples/*
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# Don't version the generated documentation
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docs/_site

‎Makefile‎

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@@ -26,6 +26,8 @@ TEST_SRCS := $(shell find src/$(PROJECT) -name "test_*.cpp")
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GTEST_SRC := src/gtest/gtest-all.cpp
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# TEST_HDRS are the test header files
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TEST_HDRS := $(shell find src/$(PROJECT) -name "test_*.hpp")
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# TOOL_SRCS are the source files for the tool binaries
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TOOL_SRCS := $(shell find tools -name "*.cpp")
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# EXAMPLE_SRCS are the source files for the example binaries
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EXAMPLE_SRCS := $(shell find examples -name "*.cpp")
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# PROTO_SRCS are the protocol buffer definitions
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PROTO_GEN_PY := ${PROTO_SRCS:.proto=_pb2.py}
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# The objects corresponding to the source files
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# These objects will be linked into the final shared library, so we
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# exclude the test and example objects.
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# exclude the tool, example, and test objects.
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CXX_OBJS := $(addprefix $(BUILD_DIR)/, ${CXX_SRCS:.cpp=.o})
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CU_OBJS := $(addprefix $(BUILD_DIR)/, ${CU_SRCS:.cu=.cuo})
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PROTO_OBJS := $(addprefix $(BUILD_DIR)/, ${PROTO_GEN_CC:.cc=.o})
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OBJS := $(PROTO_OBJS) $(CXX_OBJS) $(CU_OBJS)
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# program and test objects
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# tool, example, and test objects
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TOOL_OBJS := $(addprefix $(BUILD_DIR)/, ${TOOL_SRCS:.cpp=.o})
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EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
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TEST_OBJS := $(addprefix $(BUILD_DIR)/, ${TEST_SRCS:.cpp=.o})
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GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
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# program and test bins
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# tool, example, and test bins
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TOOL_BINS := ${TOOL_OBJS:.o=.bin}
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EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
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TEST_BINS := ${TEST_OBJS:.o=.testbin}
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@@ -91,13 +95,14 @@ PYTHON_LDFLAGS := $(LDFLAGS) $(foreach library,$(PYTHON_LIBRARIES),-l$(library))
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##############################
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# Define build targets
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##############################
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.PHONY: all init test clean linecount examples py mat distribute py$(PROJECT) mat$(PROJECT) proto
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.PHONY: all init test clean linecount tools examples py mat distribute py$(PROJECT) mat$(PROJECT) proto
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all: init $(NAME) $(STATIC_NAME) examples
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all: init $(NAME) $(STATIC_NAME) tools examples
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@echo $(CXX_OBJS)
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init:
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@ mkdir -p $(foreach obj,$(OBJS),$(dir $(obj)))
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@ mkdir -p $(foreach obj,$(TOOL_OBJS),$(dir $(obj)))
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@ mkdir -p $(foreach obj,$(EXAMPLE_OBJS),$(dir $(obj)))
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@ mkdir -p $(foreach obj,$(TEST_OBJS),$(dir $(obj)))
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@ mkdir -p $(foreach obj,$(GTEST_OBJ),$(dir $(obj)))
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test: init $(TEST_BINS)
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tools: init $(TOOL_BINS)
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examples: init $(EXAMPLE_BINS)
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py$(PROJECT): py
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$(TEST_BINS): %.testbin : %.o $(GTEST_OBJ) $(STATIC_NAME) $(TEST_HDRS)
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$(CXX) $< $(GTEST_OBJ) $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
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$(TOOL_BINS): %.bin : %.o $(STATIC_NAME)
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$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
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@echo
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$(EXAMPLE_BINS): %.bin : %.o $(STATIC_NAME)
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$(CXX) $< $(STATIC_NAME) -o $@ $(CXXFLAGS) $(LDFLAGS) $(WARNINGS)
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@echo
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$(CUDA_DIR)/bin/nvcc $(NVCCFLAGS) $(CUDA_ARCH) -c $< -o $@
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@echo
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$(BUILD_DIR)/tools/%.o: tools/%.cpp
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$(CXX) $< $(CXXFLAGS) -c -o $@ $(LDFLAGS)
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@echo
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$(BUILD_DIR)/examples/%.o: examples/%.cpp
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$(CXX) $< $(CXXFLAGS) -c -o $@ $(LDFLAGS)
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@echo
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mkdir $(DISTRIBUTE_DIR)
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# add include
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cp -r include $(DISTRIBUTE_DIR)/
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# add example binaries
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# add tool and example binaries
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mkdir $(DISTRIBUTE_DIR)/bin
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cp $(TOOL_BINS) $(DISTRIBUTE_DIR)/bin
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cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
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# add libraries
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mkdir $(DISTRIBUTE_DIR)/lib

‎data/cifar10/get_cifar10.sh‎

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#!/usr/bin/env sh
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# This scripts downloads the CIFAR10 (binary version) data and unzips it.
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd $DIR
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echo "Downloading..."
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wget -q http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
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echo "Unzipping..."
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tar -xf cifar-10-binary.tar.gz && rm -f cifar-10-binary.tar.gz
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mv cifar-10-batches-bin/* . && rm -rf cifar-10-batches-bin
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# Creation is split out because leveldb sometimes causes segfault
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# and needs to be re-created.
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echo "Done."

‎data/create_mnist.sh‎

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‎data/ilsvrc12/get_ilsvrc_aux.sh‎

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#!/usr/bin/env sh
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#
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# N.B. This does not download the ilsvrcC12 data set, as it is gargantuan.
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# This script downloads the imagenet example auxiliary files including:
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# - the ilsvrc12 image mean, binaryproto
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# - synset ids and words
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# - the training splits with labels
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd $DIR
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echo "Downloading..."
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wget -q https://www.dropbox.com/s/g5myor4y2scdv95/caffe_ilsvrc12.tar.gz
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echo "Unzipping..."
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tar -xf caffe_ilsvrc12.tar.gz && rm -f caffe_ilsvrc12.tar.gz
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echo "Done."

data/get_mnist.sh renamed to data/mnist/get_mnist.sh

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#!/usr/bin/env sh
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# This scripts downloads the mnist data and unzips it.
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DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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cd $DIR
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echo "Downloading..."
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wget -q http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz

‎data/train_mnist.sh‎

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‎docs/imagenet_pretrained.md‎

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Running Pretrained ImageNet
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===========================
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[View this page as an IPython Notebook](http://nbviewer.ipython.org/url/caffe.berkeleyvision.org/imagenet_pretrained_files/imagenet_pretrained.ipynb)
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For easier use of pretrained models, we provide a wrapper specifically written
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for the case of ImageNet, so one can take an image and directly compute features
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or predictions from them. Both Python and Matlab wrappers are provided. We will
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describe the use of the Python wrapper here, and the Matlab wrapper usage is
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very similar.
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We assume that you have successfully compiled Caffe and set the correct
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`PYTHONPATH`. If not, please refer to the [installation
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instructions](installation.html). You will use our pre-trained imagenet model,
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which you can
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[download here](https://www.dropbox.com/s/n3jups0gr7uj0dv/caffe_reference_imagenet_model)
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(232.57MB). Note that this pre-trained model is licensed for academic research /
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non-commercial use only.
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[View this page as an IPython Notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/imagenet_pretrained.ipynb)
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For easier use of pretrained models, we provide a wrapper specifically written for the case of ImageNet, so one can take an image and directly compute features or predictions from them. Both Python and Matlab wrappers are provided. We will describe the use of the Python wrapper here, and the Matlab wrapper usage is very similar.
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We assume that you have successfully compiled Caffe and set the correct `PYTHONPATH`. If not, please refer to the [installation instructions](installation.html). You will use our pre-trained imagenet model, which you can download (232.57MB) by running `models/get_caffe_reference_imagenet_model.sh`.Note that this pre-trained model is licensed for academic research / non-commercial use only.
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Ready? Let's start.
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from caffe import imagenet
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from matplotlib import pyplot
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# Set the right path to your model file, pretrained model,
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# and the image you would like to classify.
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MODEL_FILE = 'examples/imagenet_deploy.prototxt'
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PRETRAINED = '/home/jiayq/Downloads/caffe_reference_imagenet_model'
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IMAGE_FILE = '/home/jiayq/lena.png'
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MODEL_FILE = 'models/imagenet.prototxt'
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PRETRAINED = 'models/caffe_reference_imagenet_model'
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IMAGE_FILE = '/path/to/lena.png'
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Loading a network is easy. imagenet.ImagenetClassifier wraps everything. In
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default, the classifier will crop the center and corners of an image, as well as

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