Feature Extraction with Caffe

A simple python code of feature extraction with caffe refered to wellflat/cat-fancier
Including some examples classify the Oxford-IIIT Pet Dataset using scikit-learn

Dependencies

Caffe, Python 2, NumPy, scikit-learn, matplotlib

Installation

Download the CaffeNet modelfile to caffe/models/bvlc_reference_caffenet/ and other dependent files by this script

$ ./data/ilsvrc12/get_ilsvrc_aux.sh

Modify the deploy.prototxt file as following

$ cp models/bvlc_reference_caffenet/deploy.prototxt  
      models/bvlc_reference_caffenet/deploy_feature.prototxt

• deploy_feature.prototxt

# line 152
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  # top: "fc6"
  top: "fc6wi"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  # bottom: "fc6"
  bottom: "fc6wi"
  top: "fc6"
}

Usage

Make sure that the following locations (caffe_root, images, and model files) are correctly designated

• feature_extract.py

caffe_root = '../'
image_dir = caffe_root + "working/The Oxford-IIIT Pet Dataset/"
MEAN_FILE = caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy'
MODEL_FILE = caffe_root + 'models/bvlc_reference_caffenet/deploy_feature.prototxt'
PRETRAINED = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
FEAT_LAYER = 'fc6wi'

You should prepare a '.npy' file contains image filenames in numpy.array

$ python feature_extract.py -i image_filenames.npy -o extracted_features.npy

Examples

• Contour of grid search (SVM)

• Classification of Oxford-IIIT Pet Dataset using SVM

> accuracy = skl.metrics.accuracy_score(test_labels, predicts)
> print(accuracy)
0.829838709677  

> report = sklearn.metrics.classification_report(test_labels, predicts, target_names)
> print(report)
                            precision    recall  f1-score   support

          american bulldog       0.66      0.80      0.72        50
 american pit bull terrier       0.66      0.58      0.62        50
              basset hound       0.78      0.80      0.79        50
                    beagle       0.71      0.60      0.65        50
                     boxer       0.71      0.78      0.74        50
                 chihuahua       0.89      0.78      0.83        50
    english cocker spaniel       0.81      0.84      0.82        50
            english setter       0.85      0.78      0.81        50
        german shorthaired       0.95      0.82      0.88        50
            great pyrenees       0.84      0.84      0.84        50
                  havanese       0.88      0.84      0.86        50
             japanese chin       0.93      0.86      0.90        50
                  keeshond       0.98      0.98      0.98        50
                leonberger       0.88      0.92      0.90        50
        miniature pinscher       0.87      0.82      0.85        50
              newfoundland       0.85      0.92      0.88        50
                pomeranian       0.95      0.78      0.86        50
                       pug       0.96      0.98      0.97        50
             saint bernard       0.81      0.86      0.83        50
                   samoyed       0.84      0.94      0.89        50
          scottish terrier       0.90      0.90      0.90        49
                 shiba inu       0.79      0.88      0.83        50
staffordshire bull terrier       0.56      0.66      0.61        41
           wheaten terrier       0.79      0.84      0.82        50
         yorkshire terrier       0.96      0.92      0.94        50

               avg / total       0.83      0.83      0.83      1240

• Confusion matrix
  

• Keeshond (the best class in F-score)
  

• Staffordshire bull terrier (the worst class)
  

• American pit bull terrier (sometimes predicted as staffordshire bull terrier)