Predict the top Imagenet labels using a pre-trained Keras model.

predict_top_imagnet_labels.py

import numpy as np 
import keras.preprocessing.image as image_utils 
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
# You can import any other model if you would like to :)
from keras.applications.resnet50 import ResNet50 

IMG_SIZE = (224, 224) 

# Loading the model only once
CLASSIFICATION_MODEL = ResNet50()

     
def get_top_label(img_path):
    """Given an image path, return the top detected label using a ResNet50 model. """ 
    image = image_utils.load_img(img_path, target_size=IMG_SIZE) 
    image = image_utils.img_to_array(image) 
    image = np.expand_dims(image, axis=0) 
    image = preprocess_input(image) 
    preds = CLASSIFICATION_MODEL.predict(image) 
    top_label = decode_predictions(preds)[0][0][1] 
    return top_label