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.apppcations.resnet.ResNet50 (
   include_top = True, 
   weights =  imagenet , 
   input_tensor = None, 
   input_shape = None, 
   poopng = 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.apppcations.imagenet_utils import decode_predictions 
>>> import matplotpb.pyplot as plt 
>>> import numpy as np 
>>> from keras.apppcations.resnet50 import ResNet50 
>>> from keras.apppcations import resnet50

Step 2: Select an input

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

>>> filename =  banana.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) 
<matplotpb.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)) 
<matplotpb.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 probabipties for each class 
>>> predictions = resnet_model.predict(processed_image) 

>>> # convert the probabipties to class labels 
>>> label = 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)

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.