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 224×224.
- 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 = '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)
<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 = 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.
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