The Most Popular Image Classification Network to Date: A Comprehensive Ranking

Choose the image classification network you think is the most popular!

Author: Gregor Krambs
Updated on Apr 14, 2024 07:14
Welcome to StrawPoll, where your opinion matters! Are you ready to dive into the world of image classification networks and have your say on which one stands out above the rest? We've compiled a list of the most popular image classification networks to date, and we want to hear from you. Cast your vote and join thousands of others in ranking these cutting-edge technologies that are revolutionizing the way we see the world. Don't see your favorite on the list? Don't worry! You can suggest a missing option and make sure it gets the recognition it deserves. So, put on your thinking cap, get ready to engage with fellow enthusiasts and experts, and let's explore the fascinating world of image classification networks together!

What Is the Most Popular Image Classification Network to Date?

  1. 1
    58
    votes
    ResNet (short for Residual Network) is a deep neural network that has achieved state-of-the-art results in various computer vision tasks, including image classification. ResNet is known for its ability to effectively train very deep neural networks by addressing the vanishing gradient problem through the use of residual connections.
    ResNet is a deep convolutional neural network architecture that aims to address the problem of the vanishing gradient in very deep networks. It introduces skip connections that allow gradients to bypass multiple layers, enabling the training of deeper networks. ResNet achieved notable improvements in accuracy and generalization performance in image classification tasks.
    • Number of layers: Up to 152 layers
    • Skip connections: Introduced skip connections to avoid vanishing gradients
    • ImageNet top-1 accuracy: 79.3% (ResNet-152)
    • Architecture variations: ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152
    • Convolution type: Convolutional layers use 3x3 filters
  2. 2
    45
    votes
    VGGNet is a deep convolutional neural network that was developed by the Visual Geometry Group at the University of Oxford. It is known for its simplicity and effectiveness in image classification tasks, and has achieved state-of-the-art results on various benchmark datasets.
    VGGNet is a convolutional neural network (CNN) architecture used for image classification. It was proposed by Karen Simonyan and Andrew Zisserman from the University of Oxford in 2014.
    • Number of layers: 16 or 19 layers
    • Input image size: 224x224 pixels
    • Convolutional kernel size: 3x3 pixels
    • Activation function: ReLU
    • Pooling layer: Max pooling (2x2)
  3. 3
    26
    votes
    The Inception network, also known as GoogLeNet, is a deep convolutional neural network that was developed by Google researchers. It is known for its ability to perform well on a wide range of image classification tasks, and has won several competitions in computer vision.
    Inception is a popular song by Ateez, a South Korean boy band formed by KQ Entertainment. It was released on July 29, 2020, as the title track of their album 'Zero: Fever Part 1.' Inception showcases Ateez's signature energetic and powerful sound, combined with catchy melodies and impressive choreography. The song's lyrics revolve around the theme of dreams and desires, with a message of pursuing and achieving goals.
    • Release Date: July 29, 2020
    • Album: Zero: Fever Part 1
    • Genre: K-pop, EDM
    • Length: 3 minutes and 21 seconds
    • Language: Korean
  4. 4
    20
    votes
    AlexNet is a deep convolutional neural network that was developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. It was the first deep neural network to win the ImageNet Large Scale Visual Recognition Challenge in 2012, and is known for its effectiveness in image classification tasks.
    AlexNet is a convolutional neural network (CNN) architecture that revolutionized image classification tasks. It was developed by Alex Krizhevsky, Ilya Sutskever, and Geoff Hinton.
    • Year of Release: 2012
    • Number of Layers: 8
    • Number of Learned Parameters: 60 million
    • ImageNet Classification Accuracy: Top-1: 37.5%, Top-5: 17.0%
    • Input Image Size: 227x227 pixels
  5. 5
    21
    votes
    MobileNet is a family of lightweight convolutional neural networks that are designed for mobile and embedded devices. They are known for their efficiency in terms of both memory usage and computation time, and have achieved state-of-the-art results on various benchmark datasets.
    MobileNet is a highly efficient convolutional neural network (CNN) architecture designed for mobile and embedded devices. It is specifically optimized for accuracy and low computational resources usage.
    • Architecture: Depthwise separable convolutions
    • Number of layers: 28
    • Parameter size: 4.2 million
    • Image resolution: 224x224 (input size can be adjusted)
    • Performance: Top-1 accuracy of 70.6% on ImageNet dataset
  6. 6
    16
    votes
    DenseNet is a deep convolutional neural network that was developed by researchers at Facebook AI Research. It is known for its ability to effectively utilize feature maps from all preceding layers, and has achieved state-of-the-art results on various image classification tasks.
    DenseNet is a popular image classification network that was proposed by Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger in 2017. It addresses some of the limitations of traditional convolutional neural networks by introducing dense connections between layers, allowing for enhanced feature reuse and improved gradient flow throughout the network. DenseNet has gained significant popularity due to its excellent performance and efficiency on various image classification tasks.
    • Architecture: Dense connections between layers
    • Parameter Efficiency: Reduced number of total parameters
    • Feature Reuse: Enhanced feature reuse with direct connections
    • Gradient Flow: Improved gradient flow throughout the network
    • Performance: Excellent performance on various image classification tasks
  7. 7
    4
    votes
    SqueezeNet is a lightweight convolutional neural network that was developed by researchers at the University of California, Berkeley. It is known for its high accuracy and low memory footprint, and has achieved state-of-the-art results on various benchmark datasets.
    SqueezeNet is a popular image classification network known for its small model size without compromising accuracy. It achieves this by using a combination of squeezing and expanding operations to reduce the number of parameters in the network.
    • Model Size: Only 4.8 MB
    • Number of Parameters: Only 0.72 million parameters
    • Accuracy: Competitive with larger networks on benchmark datasets
    • Squeeze Excitation: Utilizes squeeze-excitation blocks to improve performance
    • Fire Modules: Introduces fire modules that combine a squeeze layer and two expand layers
  8. 8
    7
    votes
    NASNet (short for Neural Architecture Search Network) is a family of convolutional neural networks that were developed using automated neural architecture search. They are known for their high accuracy and efficiency, and have achieved state-of-the-art results on various image classification tasks.
    NASNet (Neural Architecture Search Network) is a state-of-the-art deep learning model for image classification. It was developed by Google AI (Google Brain) in collaboration with Google's AutoML project. NASNet has achieved remarkable performance and efficiency by leveraging the power of neural architecture search.
    • Number of Parameters: 88 million
    • Top-1 Accuracy: 82.7%
    • Top-5 Accuracy: 96.2%
    • Framework: TensorFlow
    • Input Size: 224x224 pixels
  9. 9
    4
    votes
    EfficientNet is a family of convolutional neural networks that were developed by researchers at Google. They are known for their high accuracy and efficiency, and have achieved state-of-the-art results on various benchmark datasets.
    EfficientNet is a popular convolutional neural network architecture for image classification that is known for its efficiency and accuracy. It was developed by Mingxing Tan and Quoc V. Le from Google Research.
    • Depth: Compound scaling allows for different depths (B0-B7) with increasing number of layers.
    • Width: Compound scaling allows for different widths (0.5-2.0) with increasing number of channels.
    • Resolution: Compound scaling allows for different input image resolutions (224-672 pixels) with increasing spatial dimensions.
    • Parameter Size: The number of parameters ranges from 5 million (B0) to over 66 million (B7).
    • Floating Point Operations: Ranges from 0.4 billion FLOPs (B0) to 6.0 billion FLOPs (B7).
  10. 10
    12
    votes
    ResNeXt is a variant of the ResNet architecture that was developed by researchers at Facebook AI Research. It is known for its ability to effectively utilize multiple paths of information flow, and has achieved state-of-the-art results on various image classification tasks.
    ResNeXt is a deep convolutional neural network architecture designed for image classification tasks. It is an extension of the ResNet architecture, developed to further improve the model's representational power by utilizing a cardinality parameter, which controls the complexity of the network.
    • Network Type: Convolutional Neural Network
    • Number of Layers: Variable, typically deep with over 100 layers
    • Cardinality: Variable, controls the complexity of the network
    • Input Size: Variable, typically 224x224 pixels
    • Activation Function: Rectified Linear Unit (ReLU)

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Ranking factors for popular image classification network

  1. Accuracy
    A high-performing image classification network should have a high accuracy rate for recognizing a wide range of images correctly. Comparing the accuracy of different networks on benchmark datasets like ImageNet can help determine their effectiveness.
  2. Robustness
    A robust network should be able to recognize images even if they are subjected to various transformations, noise, or occlusions. Robust networks are less likely to be affected by adversarial attacks and other potential pitfalls.
  3. Speed and efficiency
    An efficient network should be fast and able to process images in real-time or near-real-time. This is especially important for applications like autonomous vehicles, robotics, and video analysis, where latency can have real-world implications.
  4. Model size and complexity
    Model size and the number of parameters can impact the deployment capabilities and computational requirements of the network. Smaller models are usually easier to deploy on edge devices with limited resources, while more complex models may require specialized hardware like GPUs for accelerated processing.
  5. Transfer learning and adaptability
    Networks that can efficiently adapt to new tasks or domains by leveraging pre-trained models are highly valuable in practice, as they reduce training time and computational resources needed for fine-tuning on new data.
  6. Interpretability and explainability
    Understanding how a network makes its decisions can improve trust, help identify whether the network is likely to generalize well to new tasks, and aid in diagnosing model errors.
  7. Open-sourceness and community support
    Popular networks often have open-source implementations and active discussion forums, which helps with adoption, troubleshooting, and continuous improvement.
  8. Novelty and innovation
    Image classification networks that pioneer new techniques, architectures, or approaches may significantly advance the field and become more popular as a result.
  9. Research citations and impact
    Networks that have been widely cited in academic research, presented in conferences, or contributed significantly to the advancement of the field are usually more popular and have a better reputation.
  10. Practical applications and industry adoption
    Image classification networks that have been successfully used in real-world applications and received widespread industry adoption are more likely to be popular due to their proven effectiveness and impact.

About this ranking

This is a community-based ranking of the most popular image classification network to date. We do our best to provide fair voting, but it is not intended to be exhaustive. So if you notice something or network is missing, feel free to help improve the ranking!

Statistics

  • 1816 views
  • 213 votes
  • 10 ranked items

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Voting Rules

A participant may cast an up or down vote for each network once every 24 hours. The rank of each network is then calculated from the weighted sum of all up and down votes.

More information on most popular image classification network to date

Image classification networks have become increasingly important in the field of computer vision, with applications ranging from self-driving cars to facial recognition software. These networks use deep learning algorithms to analyze and categorize images, allowing machines to recognize objects, faces, and other visual patterns. Over the years, numerous image classification networks have been developed, each with their own strengths and weaknesses. In this article, we will explore some of the most popular image classification networks to date, examining their architectures, performance, and real-world applications.

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