updating feature extraction example

examples/feature_extraction/imagenet_val.prototxt

@@ -5,14 +5,14 @@ layers {
   top: "data"
   top: "label"
   image_data_param {
-    source: "$CAFFE_DIR/examples/_temp/file_list.txt"
+    source: "examples/_temp/file_list.txt"
     batch_size: 50
     new_height: 256
     new_width: 256
   }
   transform_param {
     crop_size: 227
-    mean_file: "$CAFFE_DIR/data/ilsvrc12/imagenet_mean.binaryproto"
+    mean_file: "data/ilsvrc12/imagenet_mean.binaryproto"
     mirror: false
   }
 }

examples/feature_extraction/readme.md

@@ -10,7 +10,9 @@ Extracting Features
 ===================
 
 In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
-Follow instructions for [installing Caffe](../../installation.html) and for [downloading the reference model](../../getting_pretrained_models.html) for ImageNet.
+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/filter_visualization.ipynb).
+
+Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.
 If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
 
 Select data to run on
@@ -35,7 +37,7 @@ Define the Feature Extraction Network Architecture
 In practice, subtracting the mean image from a dataset significantly improves classification accuracies.
 Download the mean image of the ILSVRC dataset.
 
-    data/ilsvrc12/get_ilsvrc_aux.sh
+    ./data/ilsvrc12/get_ilsvrc_aux.sh
 
 We will use `data/ilsvrc212/imagenet_mean.binaryproto` in the network definition prototxt.
 
@@ -44,14 +46,12 @@ We'll be using the `ImageDataLayer`, which will load and resize images for us.
 
     cp examples/feature_extraction/imagenet_val.prototxt examples/_temp
 
-Edit `examples/_temp/imagenet_val.prototxt` to use correct path for your setup (replace `$CAFFE_DIR`)
-
 Extract Features
 ----------------
 
 Now everything necessary is in place.
 
-    build/tools/extract_features.bin examples/imagenet/caffe_reference_imagenet_model examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10
+    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10
 
 The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
 We can use any other layer, as well, such as `conv5` or `pool3`.