Image Recognition using Pre-trained VGG16 model in Keras

Lets use a pre-trained VGG16 model to predict an image from ImageNet database. We will load an image, convert that image to numpy array, preprocess that array and let the pre-trained VGG16 model predict the image.

VGG16 is a CNN model. To know more about CNN, you can visit my this post. We are not fine-tuning the VGG16 model here. We are using it as it is. To fine-tune the existing VGG16 model, you can visit my this post.

You can download my Jupyter notebook containing following code from here.

Step 1: Import required libraries

import numpy as np
from keras.applications import vgg16
from keras.preprocessing import image

Step 2: Load pre-trained weights from VGG16 model for ImageNet dataset

model = vgg16.VGG16(weights='imagenet')

Step 3: Load image to predict

img = image.load_img('cat.jpg', target_size=(224, 224))
img

Please note that we need to reshape the image to 224X224 as it is a requirement for VGG16 model. You can download this image from ImageNet official website.

Step 4: Convert the image into numpy array

arr = image.img_to_array(img)
arr.shape

(224, 224, 3)

Step 5: Expand the array dimension

arr = np.expand_dims(arr, axis=0)
arr.shape

(1, 224, 224, 3)

Step 6: Preprocess the array

arr = vgg16.preprocess_input(arr)
arr

Step 7: Predict from the model

predictions = model.predict(arr)

predictions

We get an array as an output which is hard to understand. So, lets simplify it and see top 5 predictions made by the VGG16 model.

vgg16.decode_predictions(predictions, top=5)

[[('n02123045', 'tabby', 0.7138179),
  ('n02123159', 'tiger_cat', 0.21695374),
  ('n02124075', 'Egyptian_cat', 0.043560617),
  ('n04040759', 'radiator', 0.0053847637),
  ('n04553703', 'washbasin', 0.0024860944)]]

So, as per VGG16 model prediction, the given image may be a tabby (71%) or a tiger cat (21%). You can try the same with different images from ImageNet database and check your results.