Simple Image Classification with ResNet-50

5 min readNov 22, 2019

Authors: Marie-Louise Christensen, Nina Danielsen, Pernille Franzen og Lisa Bro Nilsen.

Doing cool things with data doesn’t always need to be difficult. By using ResNet-50 you don’t have to start from scratch when it comes to building a classifier model and make a prediction based on it. This article is an beginners guide to ResNet-50. In the following you will get an short overall introduction to ResNet-50 and a simple tutorial on how to use it for image classification with python coding.

What is ResNet-50 and why use it for image classification?

ResNet-50 is a pretrained Deep Learning model for image classification of the Convolutional Neural Network(CNN, or ConvNet), which is a class of deep neural networks, most commonly applied to analyzing visual imagery. ResNet-50 is 50 layers deep and is trained on a million images of 1000 categories from the ImageNet database. Furthermore the model has over 23 million trainable parameters, which indicates a deep architecture that makes it better for image recognition. Using a pretrained model is a highly effective approach, compared if you need to build it from scratch, where you need to collect great amounts of data and train it yourself. Of course, there are other pretrained deep models to use such as AlexNet, GoogleNet or VGG19, but the ResNet-50 is noted for excellent generalization performance with fewer error rates on recognition tasks and is therefore a useful tool to know.

Architecture of ResNet-50

ResNet stands for Residual Network and more specifically it is of a Residual Neural Network architecture. What characterizes a residual network is its identity connections. Identity connections takes the input directly to the end of each residual block, as shown below with the curved arrow:

figure 1: (Picture Credit: https://en.wikipedia.org/wiki/Residual_neural_network)

Specifically, the ResNet-50 model consists of 5 stages each with a residual block. Each residual block has 3 layers with both 1*1 and 3*3 convolutions. The concept of residual blocks is quite simple. In traditional neural networks, each layer feeds into the next layer. In a network with residual blocks, each layer feeds into the next layer and directly into the layers about 2–3 hops away, called identity connections.

Classification case: Labrador Retriever

In this example we are using Google Colaboratory to do the code. You can use this link to get an introduction to Colab and create your own code in python: https://colab.research.google.com/notebooks/welcome.ipynb

When using colab, first, remember to change the runtime to GPU or else it will take a while when working with these deep learning networks.

Lets get started!

Step 1: Importing the necessary libraries

First step is to import the necessary libraries needed to classify the images. In this case you need to use numpy, keras and matplotlib.

#Copy the Code HERE!
import numpy as np
from keras.preprocessing.image import image
from keras.preprocessing.image import img_to_array
from keras.applications.resnet50 import preprocess_input
from keras.applications.imagenet_utils import decode_predictions
import matplotlib.pyplot as plt

Step 2: Read and unzip the file

To download the dataset file into Google Colab it is useful to use the !wget command.Then it will be added to your file repository in colab and from there you can create a path way to the image or dataset you want to use.

Next is to unzip the file using the !unzip command and the full name of the file.

There are certainly other ways to load your dataset, but we find this to be the most simple way.

#Copy the Code HERE!:
!wget -qq http://sds-datacrunch.aau.dk/public/dataset.zip
!unzip -qq dataset.zip

Step 3: Preprocessing images for ResNet-50

Load a picture from the dataset before starting the preprocessing. When loading the picture remenber to set the right target size which for ResNet is 224*224.

Now the image is readable and it can be plotted.

figure 4: loading and visualization of the image

Keras is expecting a list of images which is why we need to turn it into an array and then add another dimension to this. Following this we need to normalize the image using preprocess input method.

#Copy the code HERE!
img = image.load_img(‘dataset/single_prediction/cat_or_dog_1.jpg’, target_size = (224, 224))
plt.imshow(img)
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = preprocess_input(img)

Step 4: Make a prediction using the ResNet-50 model in Keras

After preprocessing the image you can start classifying by simply instantiating the ResNet-50 model.

figure 6: creating a model

Now use the model.predict function on the preprocessed image which is called ‘img’.

To also print the prediction, use the print function and put the decode_prediction in the print argument and ‘preds’ in the decode_prediction argument.