image classification MobileNetV2 TensorFlow

To use a pre-trained MobileNetV2 model from ImageNet as a feature extractor to classify a large number of images, you can use the TensorFlow or Keras library in Python. Here is a simple example of how you can do this:

import tensorflow as tf
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input
from tensorflow.keras.preprocessing import image
import numpy as np

# Load the pre-trained model
model = MobileNetV2(weights='imagenet')

def classify_image(image_path):
    # Load and preprocess the image
    img = image.load_img(image_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    # Classify the image
    preds = model.predict(x)
    return preds

# Classify a new image
image_path = 'path_to_your_image.jpg'
preds = classify_image(image_path)
print('Predicted:', tf.keras.applications.mobilenet_v2.decode_predictions(preds, top=3)[0])

In this code, the MobileNetV2 model is loaded with weights pre-trained on ImageNet. The classify_image function loads an image from the specified path, preprocesses it to the format expected by the model (224x224 pixels and pixel values in the range -1 to 1), and then uses the model to classify the image. The decode_predictions function is used to decode the model's output into human-readable labels[2][7][12].

Please replace 'path_to_your_image.jpg' with the actual path to the image you want to classify. If you have a large number of images to classify, you can call the classify_image function in a loop.

Remember that the MobileNetV2 model was trained to classify images into 1000 categories, so it may not perform well if your images belong to categories not included in the ImageNet dataset[11]. If that's the case, you might need to fine-tune the model on your specific dataset[4][7].

Citations:
[1] https://github.com/topics/mobilenetv2?l=python&o=desc&s=updated
[2] https://pythontutorials.eu/deep-learning/image-classification/
[3] https://www.kaggle.com/code/darthmanav/mobilenet-v2-feature-extraction
[4] https://blog.roboflow.com/how-to-train-mobilenetv2-on-a-custom-dataset/
[5] https://www.kaggle.com/code/jonathansilva2020/mobilenetv2-image-classification-train
[6] https://www.kaggle.com/code/omkarmodi/mobilenetv2-feature-extraction
[7] https://www.tensorflow.org/tutorials/images/transfer_learning
[8] https://www.e2enetworks.com/blog/step-by-step-guide-to-image-classification-using-mobilenetv2
[9] https://stackoverflow.com/questions/68523007/how-to-use-mobilenet-as-feature-extractor-for-high-resolution-images
[10] https://www.kaggle.com/code/gopalrahulrg/mobilenet-feature-extraction
[11] https://nl.mathworks.com/help/deeplearning/ref/mobilenetv2.html
[12] https://keras.io/api/applications/mobilenet/
[13] https://www.youtube.com/watch?v=8LjK4knsTRQ
[14] https://developer.arm.com/documentation/102561/latest/Overview/Image-classification
[15] https://huggingface.co/docs/timm/models/mobilenet-v2
[16] https://python.plainenglish.io/unlocking-the-power-of-transfer-learning-in-image-classification-with-pytorch-d3a8e9179cea?gi=eb2f5a7759b1
[17] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019605/
[18] https://www.youtube.com/watch?v=w8Qx40tHeEM
[19] https://www.alibabacloud.com/blog/part-3-image-classification-using-features-extracted-by-transfer-learning-in-keras_595291