Run TensorFlow model in Python

After you have exported your TensorFlow model from the Custom Vision Service, this quickstart will show you how to use this model locally to classify images.

Install required components


To use the tutorial, you need to do the following:

  • Install either Python 2.7+ or Python 3.5+.
  • Install pip.

Also you will need to install the following packages:

pip install tensorflow
pip install pillow
pip install numpy
pip install opencv-python

Load your model and tags

The downloaded zip file contains a model.pb and a labels.txt. These files represent the trained model and the classification labels. The first step is to load the model into your project.

import tensorflow as tf
import os

graph_def = tf.GraphDef()
labels = []

# Import the TF graph
with tf.gfile.FastGFile(filename, 'rb') as f:
    tf.import_graph_def(graph_def, name='')

# Create a list of labels.
with open(labels_filename, 'rt') as lf:
    for l in lf:

Prepare an image for prediction

There are a few steps for preparing the image so that it's the right shape for prediction. These steps mimic the image manipulation performed during training:

Open the file and create an image in the BGR color space

from PIL import Image
import numpy as np
import cv2

# Load from a file
imageFile = "<path to your image file>"
image =

# Update orientation based on EXIF tags, if the file has orientation info.
image = update_orientation(image)

# Convert to OpenCV format
image = convert_to_opencv(image)

Deal with images with a dimension >1600

# If the image has either w or h greater than 1600 we resize it down respecting
# aspect ratio such that the largest dimension is 1600
image = resize_down_to_1600_max_dim(image)

Crop the largest center square

# We next get the largest center square
h, w = image.shape[:2]
min_dim = min(w,h)
max_square_image = crop_center(image, min_dim, min_dim)

Resize down to 256x256

# Resize that square down to 256x256
augmented_image = resize_to_256_square(max_square_image)

Crop the center for the specific input size for the model

# The compact models have a network size of 227x227, the model requires this size.
network_input_size = 227

# Crop the center for the specified network_input_Size
augmented_image = crop_center(augmented_image, network_input_size, network_input_size)

The steps above use the following helper functions:

def convert_to_opencv(image):
    # RGB -> BGR conversion is performed as well.
    r,g,b = np.array(image).T
    opencv_image = np.array([b,g,r]).transpose()
    return opencv_image

def crop_center(img,cropx,cropy):
    h, w = img.shape[:2]
    startx = w//2-(cropx//2)
    starty = h//2-(cropy//2)
    return img[starty:starty+cropy, startx:startx+cropx]

def resize_down_to_1600_max_dim(image):
    h, w = image.shape[:2]
    if (h < 1600 and w < 1600):
        return image

    new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
    return cv2.resize(image, new_size, interpolation = cv2.INTER_LINEAR)

def resize_to_256_square(image):
    h, w = image.shape[:2]
    return cv2.resize(image, (256, 256), interpolation = cv2.INTER_LINEAR)

def update_orientation(image):
    exif_orientation_tag = 0x0112
    if hasattr(image, '_getexif'):
        exif = image._getexif()
        if (exif != None and exif_orientation_tag in exif):
            orientation = exif.get(exif_orientation_tag, 1)
            # orientation is 1 based, shift to zero based and flip/transpose based on 0-based values
            orientation -= 1
            if orientation >= 4:
                image = image.transpose(Image.TRANSPOSE)
            if orientation == 2 or orientation == 3 or orientation == 6 or orientation == 7:
                image = image.transpose(Image.FLIP_TOP_BOTTOM)
            if orientation == 1 or orientation == 2 or orientation == 5 or orientation == 6:
                image = image.transpose(Image.FLIP_LEFT_RIGHT)
    return image

Predict an image

Once the image is prepared as a tensor we can send it through the model for a prediction:

# These names are part of the model and cannot be changed.
output_layer = 'loss:0'
input_node = 'Placeholder:0'

with tf.Session() as sess:
    prob_tensor = sess.graph.get_tensor_by_name(output_layer)
    predictions, =, {input_node: [augmented_image] })

View the results

The results of running the image tensor through the model will then need to be mapped back to the labels.

    # Print the highest probability label
    highest_probability_index = np.argmax(predictions)
    print('Classified as: ' + labels[highest_probability_index])

    # Or you can print out all of the results mapping labels to probabilities.
    label_index = 0
    for p in predictions:
        truncated_probablity = np.float64(round(p,8))
        print (labels[label_index], truncated_probablity)
        label_index += 1

Next steps

You can also wrap the model into a mobile application: