Real Time Prediction using ResNet Model

ResNet is a pre-trained model. It is trained using ImageNet. ResNet model weights pre-trained on ImageNet. It has the following syntax −

keras.applications.resnet.ResNet50 (
include_top = True,
weights = 'imagenet',
input_tensor = None,
input_shape = None,
pooling = None,
classes = 1000
)

Here,

• include_top refers the fully-connected layer at the top of the network.
• weights refer pre-training on ImageNet.
• input_tensor refers optional Keras tensor to use as image input for the model.
• input_shape refers optional shape tuple. The default input size for this model is 224x224.
• classes refer optional number of classes to classify images.
  Let us understand the model by writing a simple example −

  Step 1: import the modules

  Let us load the necessary modules as specified below −

  >>> import PIL
  >>> from keras.preprocessing.image import load_img
  >>> from keras.preprocessing.image import img_to_array
  >>> from keras.applications.imagenet_utils import decode_predictions
  >>> import matplotlib.pyplot as plt
  >>> import numpy as np
  >>> from keras.applications.resnet50 import ResNet50
  >>> from keras.applications import resnet50

  Step 2: Select an input

  Let us choose an input image, Lotus as specified below −

  >>> filename = 'grapes.jpg'
  >>> ## load an image in PIL format
  >>> original = load_img(filename, target_size = (224, 224))
  >>> print('PIL image size',original.size)
  PIL image size (224, 224)
  >>> plt.imshow(original)
  <matplotlib.image.AxesImage object at 0x1304756d8>
  >>> plt.show()

  Here, we have loaded an image (banana.jpg) and displayed it.

  Step 3: Convert images into NumPy array

  Let us convert our input, Banana into NumPy array, so that it can be passed into the model for the purpose of prediction.

  >>> #convert the PIL image to a numpy array
  >>> numpy_image = img_to_array(original)
  >>> plt.imshow(np.uint8(numpy_image))
  <matplotlib.image.AxesImage object at 0x130475ac8>
  >>> print('numpy array size',numpy_image.shape)
  numpy array size (224, 224, 3)
  >>> # Convert the image / images into batch format
  >>> image_batch = np.expand_dims(numpy_image, axis = 0)
  >>> print('image batch size', image_batch.shape)
  image batch size (1, 224, 224, 3)
  >>>

  Step 4: Model prediction

  Let us feed our input into the model to get the predictions

  >>> prepare the image for the resnet50 model >>>
  >>> processed_image = resnet50.preprocess_input(image_batch.copy())
  >>> # create resnet model
  >>>resnet_model = resnet50.ResNet50(weights = 'imagenet')
  >>> Downloavding data from https://github.com/fchollet/deep-learning-models/releas
  es/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5
  102858752/102853048 [==============================] - 33s 0us/step
  >>> # get the predicted probabilities for each class
  >>> predictions = resnet_model.predict(processed_image)
  >>> # convert the probabilities to class labels
  >>> label_data = decode_predictions(predictions)
  Downloading data from https://storage.googleapis.com/download.tensorflow.org/
  data/imagenet_class_index.json
  40960/35363 [==================================] - 0s 0us/step
  >>> print(label_data)

  Output

  [
  [
  ('n07753592', 'banana', 0.99229723),
  ('n03532672', 'hook', 0.0014551596),
  ('n03970156', 'plunger', 0.0010738898),
  ('n07753113', 'fig', 0.0009359837) ,
  ('n03109150', 'corkscrew', 0.00028538404)
  ]
  ]

  Here, the model predicted the images as banana correctly.