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75 lines
3.0 KiB
Markdown
75 lines
3.0 KiB
Markdown
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---
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title: Feature extraction with Caffe C++ code.
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description: Extract CaffeNet / AlexNet features using the Caffe utility.
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category: example
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include_in_docs: true
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priority: 10
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---
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Extracting Features
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===================
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In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
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Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
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Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.
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If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
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Select data to run on
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---------------------
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We'll make a temporary folder to store things into.
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mkdir examples/_temp
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Generate a list of the files to process.
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We're going to use the images that ship with caffe.
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find `pwd`/examples/images -type f -exec echo {} \; > examples/_temp/temp.txt
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The `ImageDataLayer` we'll use expects labels after each filenames, so let's add a 0 to the end of each line
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sed "s/$/ 0/" examples/_temp/temp.txt > examples/_temp/file_list.txt
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Define the Feature Extraction Network Architecture
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--------------------------------------------------
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In practice, subtracting the mean image from a dataset significantly improves classification accuracies.
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Download the mean image of the ILSVRC dataset.
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./data/ilsvrc12/get_ilsvrc_aux.sh
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We will use `data/ilsvrc212/imagenet_mean.binaryproto` in the network definition prototxt.
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Let's copy and modify the network definition.
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We'll be using the `ImageDataLayer`, which will load and resize images for us.
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cp examples/feature_extraction/imagenet_val.prototxt examples/_temp
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Extract Features
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----------------
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Now everything necessary is in place.
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./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb
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The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
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We can use any other layer, as well, such as `conv5` or `pool3`.
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The last parameter above is the number of data mini-batches.
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The features are stored to LevelDB `examples/_temp/features`, ready for access by some other code.
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If you meet with the error "Check failed: status.ok() Failed to open leveldb examples/_temp/features", it is because the directory examples/_temp/features has been created the last time you run the command. Remove it and run again.
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rm -rf examples/_temp/features/
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If you'd like to use the Python wrapper for extracting features, check out the [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
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Clean Up
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--------
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Let's remove the temporary directory now.
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rm -r examples/_temp
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