MobileNetV3Large tensorflow

MobileNetV3Large is a popular convolutional neural network architecture that is designed for mobile and embedded devices. It was introduced by Google in their paper titled "Searching for MobileNetV3" in 2019. MobileNetV3Large is an extension of the MobileNetV2 architecture, with improved performance and efficiency.

In TensorFlow, you can use the MobileNetV3Large architecture by importing the pre-trained model from the TensorFlow Hub or implementing it from scratch using the TensorFlow API. Here's an example code snippet to load the pre-trained MobileNetV3Large model from TensorFlow Hub:

import tensorflow as tf
import tensorflow_hub as hub

# Load the MobileNetV3Large model from TensorFlow Hub
module = hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/feature_vector/5", trainable=False)

# Create a model using MobileNetV3Large as the base
model = tf.keras.Sequential([
    module,
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

# Compile and train the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_dataset, validation_data=val_dataset, epochs=num_epochs)

Make sure you replace `num_classes`, `train_dataset`, `val_dataset`, and `num_epochs` with your own values specific to your task.

Remember that MobileNetV3Large is a powerful architecture for image classification tasks, but it may need to be fine-tuned or adjusted depending on the specific requirements of your project.