python -m caffe2.python.models.download --install squeezenet

sudo PYTHONPATH=/usr/local python -m caffe2.python.models.download --install squeezenet

# load up the caffe2 workspace
from caffe2.python import workspace
# choose your model here (use the downloader first)
from caffe2.python.models import squeezenet as mynet
# helper image processing functions
import caffe2.python.tutorials.helpers as helpers

import skimage.io 
from matplotlib import pyplot as plt

# load the pre-trained model
init_net = mynet.init_net
predict_net = mynet.predict_net
# you must name it something
predict_net.name = "squeezenet_predict"
workspace.RunNetOnce(init_net)
workspace.CreateNet(predict_net)
p = workspace.Predictor(init_net.SerializeToString(), predict_net.SerializeToString())

# use whatever image you want (local files or urls)
#img_pth = "https://upload.wikimedia.org/wikipedia/commons/thumb/7/7b/Orange-Whole-%26-Split.jpg/1200px-Orange-Whole-%26-Split.jpg"
#img_pth = "https://upload.wikimedia.org/wikipedia/commons/a/ac/Pretzel.jpg"
img_pth = "https://cdn.pixabay.com/photo/2015/02/10/21/28/flower-631765_1280.jpg"
# average mean to subtract from the image
mean = 128
# the size of images that the model was trained with
input_size = 227

# use the image helper to load the image and convert it to NCHW
img = helpers.loadToNCHW(img_pth, mean, input_size)

# submit the image to net and get a tensor of results

results = p.run([img])
response = helpers.parseResults(results)
# and lookup our result from the list
print response

#show result on image
img_mat = skimage.io.imread(img_pth)
skimage.io.imshow(img_mat)
plt.title(response,{‘fontsize‘: ‘20‘})
plt.savefig(‘pretzel.jpg‘)
plt.show()