Introduction

This example demonstrates using a network pretrained on ImageNet for classification. The model used was converted from the VGG_CNN_S model (http://arxiv.org/abs/1405.3531) in Caffe's Model Zoo.

For details of the conversion process, see the example notebook "Using a Caffe Pretrained Network - CIFAR10".

License

The model is licensed for non-commercial use only

Download the model (393 MB)



In [ ]:

    
!wget https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg_cnn_s.pkl

Setup



In [2]:

    
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline



In [ ]:

    
import lasagne
from lasagne.layers import InputLayer, DenseLayer, DropoutLayer
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
from lasagne.layers import MaxPool2DLayer as PoolLayer
from lasagne.layers import LocalResponseNormalization2DLayer as NormLayer
from lasagne.utils import floatX

Define the network



In [4]:

    
net = {}
net['input'] = InputLayer((None, 3, 224, 224))
net['conv1'] = ConvLayer(net['input'], num_filters=96, filter_size=7, stride=2, flip_filters=False)
net['norm1'] = NormLayer(net['conv1'], alpha=0.0001) # caffe has alpha = alpha * pool_size
net['pool1'] = PoolLayer(net['norm1'], pool_size=3, stride=3, ignore_border=False)
net['conv2'] = ConvLayer(net['pool1'], num_filters=256, filter_size=5, flip_filters=False)
net['pool2'] = PoolLayer(net['conv2'], pool_size=2, stride=2, ignore_border=False)
net['conv3'] = ConvLayer(net['pool2'], num_filters=512, filter_size=3, pad=1, flip_filters=False)
net['conv4'] = ConvLayer(net['conv3'], num_filters=512, filter_size=3, pad=1, flip_filters=False)
net['conv5'] = ConvLayer(net['conv4'], num_filters=512, filter_size=3, pad=1, flip_filters=False)
net['pool5'] = PoolLayer(net['conv5'], pool_size=3, stride=3, ignore_border=False)
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
net['drop6'] = DropoutLayer(net['fc6'], p=0.5)
net['fc7'] = DenseLayer(net['drop6'], num_units=4096)
net['drop7'] = DropoutLayer(net['fc7'], p=0.5)
net['fc8'] = DenseLayer(net['drop7'], num_units=1000, nonlinearity=lasagne.nonlinearities.softmax)
output_layer = net['fc8']

Load the model parameters and metadata



In [5]:

    
import pickle

model = pickle.load(open('vgg_cnn_s.pkl'))
CLASSES = model['synset words']
MEAN_IMAGE = model['mean image']

lasagne.layers.set_all_param_values(output_layer, model['values'])

Trying it out

Get some test images

We'll download the ILSVRC2012 validation URLs and pick a few at random



In [6]:

    
import urllib

index = urllib.urlopen('http://www.image-net.org/challenges/LSVRC/2012/ori_urls/indexval.html').read()
image_urls = index.split('<br>')

np.random.seed(23)
np.random.shuffle(image_urls)
image_urls = image_urls[:5]

Helper to fetch and preprocess images



In [7]:

    
import io
import skimage.transform

def prep_image(url):
    ext = url.split('.')[-1]
    im = plt.imread(io.BytesIO(urllib.urlopen(url).read()), ext)
    # Resize so smallest dim = 256, preserving aspect ratio
    h, w, _ = im.shape
    if h < w:
        im = skimage.transform.resize(im, (256, w*256/h), preserve_range=True)
    else:
        im = skimage.transform.resize(im, (h*256/w, 256), preserve_range=True)

    # Central crop to 224x224
    h, w, _ = im.shape
    im = im[h//2-112:h//2+112, w//2-112:w//2+112]
    
    rawim = np.copy(im).astype('uint8')
    
    # Shuffle axes to c01
    im = np.swapaxes(np.swapaxes(im, 1, 2), 0, 1)
    
    # Convert to BGR
    im = im[::-1, :, :]

    im = im - MEAN_IMAGE
    return rawim, floatX(im[np.newaxis])

Process test images and print top 5 predicted labels



In [8]:

    
for url in image_urls:
    try:
        rawim, im = prep_image(url)

        prob = np.array(lasagne.layers.get_output(output_layer, im, deterministic=True).eval())
        top5 = np.argsort(prob[0])[-1:-6:-1]

        plt.figure()
        plt.imshow(rawim.astype('uint8'))
        plt.axis('off')
        for n, label in enumerate(top5):
            plt.text(250, 70 + n * 20, '{}. {}'.format(n+1, CLASSES[label]), fontsize=14)
    except IOError:
        print('bad url: ' + url)