Top Five Deep Learning Models in TensorFlow and Its Relevance in 2024 | by Ibtihajahmadkhan | Jul, 2024

TensorFlow is an open-source deep learning framework by Google. It has become one of the top libraries to develop machine learning and deep learning models. By 2024, TensorFlow has established itself as a prominent and potent tool for creating, training, and implementing machine learning models, especially in the field of image processing. Combined with the processing power of Nvidia, Tensorflow is a heaven for researchers, AI enthusiasts and developers.

TensorFlow’s significance is emphasised by its comprehensive collection of pre-existing models and tools that streamline the intricacies inherent in deep learning jobs. Standard extensive datasets have been used to train the built-in models, providing developers with a substantial advantage by delivering high levels of accuracy and efficiency right from the start. As we dig into 2024, TensorFlow continues to be a vital tool, driving developments in deep learning and allowing new applications in image analysis and beyond. Following are the top most well-known models from TensorFlow.

EfficientNetV2 represents a significant advancement in the EfficientNet series, aiming to optimize both efficiency and performance in image classification tasks. What sets EfficientNetV2 apart is its use of a compound scaling method, which uniformly scales network dimensions such as depth, width, and resolution. This balanced scaling approach enhances the model’s ability to generalize across various tasks while maintaining computational efficiency.

The architecture of EfficientNetV2 incorporates several innovations. It introduces a new family of mobile inverted bottleneck convolution (MBConv) layers, which are designed to reduce the number of parameters while preserving accuracy. Additionally, EfficientNetV2 leverages fused MBConv layers for further efficiency gains and improved training dynamics. This architecture makes it ideal for deployment in environments with limited computational resources, such as mobile devices and edge computing platforms.

In TensorFlow, EfficientNetV2 can be implemented using the tf.keras.applications module. The primary function for this implementation is EfficientNetV2, which comes with several key arguments to tailor the model to specific needs:

• input_shape: This argument defines the shape of the input tensor. It is crucial for aligning the model’s dimensions with the data being used.
• include_top: When set to True, this argument includes the fully connected layer at the top of the network. For transfer learning purposes, setting this to False allows customization of the top layers.
• weights: This argument specifies the pre-trained weights to be loaded. Options include 'imagenet' for ImageNet weights or None for random initialization.
• classes: This defines the number of output classes for the classification task, aligning the model output with the specific application requirements.

Here is an example of how to instantiate EfficientNetV2 in TensorFlow:

from tensorflow.keras.applications import EfficientNetV2model = EfficientNetV2(    input_shape=(224, 224, 3),    include_top=True,    weights='imagenet',    classes=1000)

The Vision Transformer (ViT) represents a significant shift in image classification, utilizing transformer architecture rather than traditional Convolutional Neural Networks (CNNs). This model treats image patches as sequences, similar to words in natural language processing, allowing it to capture long-range dependencies more effectively. ViT excels in scenarios where large datasets are available, offering improved performance and scalability.

Key features of the Vision Transformer include its ability to handle larger receptive fields and reduced inductive biases, which can lead to better generalization on diverse image datasets. The model’s structure comprises multiple layers of transformer encoders, each processing image patches and refining the feature representations.

In TensorFlow, the ViT model can be deployed using the `tf.keras.applications.VisionTransformer` function. This function allows customization through various arguments, facilitating flexibility and optimization for specific tasks. Here are the key arguments:

• input_shape: Defines the shape of the input images, typically in the format (height, width, channels).
• patch_size: Specifies the size of the image patches to be processed by the transformer.
• num_layers: Determines the number of transformer layers in the model.
• d_model: Sets the dimensionality of the encoder layers and the feed-forward layers.
• num_heads: Indicates the number of attention heads in each transformer layer, enabling multi-head attention.

import tensorflow as tfvit_model = tf.keras.applications.VisionTransformer(    input_shape=(224, 224, 3),    patch_size=16,    num_layers=12,    d_model=768,    num_heads=12)

This code snippet demonstrates how to instantiate a Vision Transformer model using TensorFlow’s built-in function. The `input_shape` parameter is set to (224, 224, 3), which is standard for many image classification tasks. The `patch_size` of 16 divides the input image into 16×16 patches. `num_layers` is set to 12, indicating the number of transformer layers, while `d_model` and `num_heads` are configured to 768 and 12, respectively, aligning with common configurations for ViT models.

ResNet50V2 represents a significant enhancement over the original ResNet50 model, incorporating several advanced features that make it a favored choice among deep learning practitioners. One of the primary improvements in ResNet50V2 is the introduction of modifications in the residual blocks, which help to mitigate the vanishing gradient problem more effectively. This allows for the training of deeper networks without significant degradation in performance, thereby facilitating the development of more sophisticated and accurate models.

The ResNet50V2 model also benefits from an improved architecture that includes identity mappings, making it easier to optimize and less prone to overfitting. These enhancements lead to better convergence during training and improved generalization on test data. Moreover, the updated design achieves higher accuracy while maintaining computational efficiency, making it a practical choice for various image recognition tasks.

In TensorFlow, the ResNet50V2 model can be easily utilized through a built-in function, which offers flexibility with its arguments. Here is a breakdown of the key arguments:

• input_shape: This argument specifies the shape of the input data, typically a tuple of three integers (height, width, channels). It ensures that the model is compatible with the dimensions of the input images.
• include_top: A boolean argument that determines whether to include the fully-connected layer at the top of the network. Setting it to False allows for customization of the model’s output layer, which is useful for transfer learning.
• weights: This argument allows you to specify pre-trained weights, such as ‘imagenet’. Utilizing pre-trained weights can significantly accelerate the training process and improve model performance.
• pooling: This argument defines the type of pooling operation to be applied to the output of the last convolutional layer. Options include ‘max’ or ‘avg’, which can influence the model’s final performance.

The following code snippet demonstrates how to implement the ResNet50V2 model in TensorFlow:

from tensorflow.keras.applications import ResNet50V2model = ResNet50V2(    input_shape=(224, 224, 3),    include_top=False,    weights='imagenet',    pooling='avg')

By leveraging the ResNet50V2 model and its advanced features, deep learning practitioners can achieve superior performance in image recognition tasks, with the added benefit of a more robust and efficient training process.

MobileNetV3, introduced by Google, stands out for its efficiency and performance, particularly in mobile and edge device scenarios. This model strikes a fine balance between latency and accuracy, making it an ideal choice for applications where computational resources are limited. One of the key architectural innovations in MobileNetV3 is the use of a combination of squeeze-and-excitation modules and hard-swish activation functions. These enhancements contribute to improved computational efficiency without compromising on performance.

Another noteworthy feature of MobileNetV3 is its two variants: MobileNetV3-Large and MobileNetV3-Small. The Large variant is optimized for high accuracy, while the Small variant is designed for resource-constrained environments, making it suitable for real-time applications on mobile devices.

In TensorFlow, MobileNetV3 can be easily utilized through the tf.keras.applications.MobileNetV3 function. This function allows users to tailor the model to their specific needs by adjusting various arguments:

• input_shape: Defines the dimensions of the input tensor. Commonly set as (224, 224, 3) for typical image inputs.
• alpha: Controls the width of the network. Values less than 1.0 reduce the number of filters, leading to a lightweight model.
• include_top: Determines whether to include the fully-connected layer at the top of the network. Setting this to False is useful for transfer learning.
• weights: Specifies the pre-trained weights to be loaded. Options include ‘imagenet’ for weights pre-trained on ImageNet or None for random initialization.

The following example demonstrates how to instantiate MobileNetV3 in TensorFlow:

from tensorflow.keras.applications import MobileNetV3
model = MobileNetV3(input_shape=(224, 224, 3), alpha=1.0, include_top=True, weights='imagenet')

By leveraging MobileNetV3, developers can deploy efficient deep learning models on mobile and edge devices, benefiting from its optimized architecture and configurable parameters to suit diverse application needs.

NASNetMobile is a sophisticated deep learning model developed through Neural Architecture Search (NAS) techniques. It is specifically optimized for mobile devices, making it an ideal choice for applications requiring efficient and effective image processing on-the-go. The unique aspect of NASNetMobile lies in its ability to balance performance and resource consumption, thus ensuring that even mobile devices with limited computational power can achieve remarkable image recognition capabilities.

One of the primary benefits of NASNetMobile is its flexibility and adaptability. The model is designed to scale efficiently, providing high accuracy while minimizing latency and power consumption. This makes NASNetMobile suitable for a wide range of applications, from real-time image classification to augmented reality and beyond.

In TensorFlow, NASNetMobile can be easily implemented using the `tf.keras.applications.NASNetMobile` function. This function allows you to customize several important parameters to suit your specific needs:

input_shape: This argument specifies the shape of the input data. For instance, an input shape of (224, 224, 3) would be appropriate for standard RGB images of size 224×224 pixels.

include_top: This boolean argument determines whether the fully-connected layer at the top of the network should be included. Setting this to `False` allows you to customize the model for tasks like feature extraction or transfer learning.

weights: This argument denotes the pre-trained weights to be loaded into the model. Using ‘imagenet’ will load weights pre-trained on the ImageNet dataset, which can be beneficial for a wide range of image classification tasks.

classes: This argument specifies the number of output classes for the model. It is particularly useful when customizing the model for specific classification tasks.

Here is an example code block to implement NASNetMobile in TensorFlow:

import tensorflow as tf    model = tf.keras.applications.NASNetMobile(input_shape=(224, 224, 3),                                               include_top=True,                                               weights='imagenet',                                               classes=1000)

By leveraging the NASNetMobile architecture, developers can create powerful and efficient image recognition applications designed to operate seamlessly on mobile devices. Its combination of high performance and low resource consumption makes it a valuable tool in the realm of mobile deep learning.

The exploration of TensorFlow’s built-in deep learning models highlights their significant advantages in 2024 compared to their competitors such as Pytorch. These models provide streamlined, efficient solutions for tackling complex image-related deep learning tasks, from classification to object detection and segmentation.

Throughout the blog, we have discussed the strengths of the top models such as InceptionV3, MobileNetV2, EfficientNet, ResNet50, and NASNet. Each model offers unique benefits, catering to various needs such as speed, accuracy, and adaptability. InceptionV3 and ResNet50 are renowned for their high accuracy in image classification tasks, while MobileNetV2 and EfficientNet excel in providing lightweight solutions suitable for mobile and edge devices. NASNet, with its neural architecture search, introduces a new paradigm in model optimization.

Looking ahead, the field of deep learning for image processing is poised for further advancements. Emerging trends suggest a focus on enhancing model efficiency, reducing computational load, and increasing real-time processing capabilities. TensorFlow is expected to continue evolving, integrating more sophisticated algorithms, and expanding its suite of tools to support these advancements. Innovations such as automated machine learning (AutoML) and more refined transfer learning techniques will likely play pivotal roles in this evolution.