AlexNet

Extracted Attributes

• Type

  Convolutional Neural Network (CNN) architecture

• Field

  Deep Learning, Computer Vision, Image Classification

• Neurons

  650,000

• Developer

  Geoffrey Hinton

• Parameters

  60 million

• Affiliation

  University of Toronto

• Key Innovation

  Data augmentation

• Year Developed

  2012

• Architecture Layers

  8 (5 convolutional, 3 fully connected)

• ILSVRC Top-5 Error Rate

  15.3%

• Citations (as of early 2025)

  Over 172,000 (Google Scholar)

• Image Classification Categories

  1,000

• ILSVRC Runner-up Top-5 Error Rate

  26.2%

Timeline

• Creation of the ImageNet dataset began by Fei-Fei Li and collaborators, which later became central to AlexNet's success. (Source: web_search_results)

  2007

• AlexNet convolutional neural network architecture developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. (Source: summary, wikipedia, dbpedia, web_search_results)

  2012

• AlexNet, submitted by team SuperVision, won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) with a top-5 error rate of 15.3%. (Source: wikipedia, dbpedia, web_search_results)

  2012-09-30

AlexNet

AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). It classifies images into 1,000 distinct object categories and is regarded as the first widely recognized application of deep convolutional networks in large-scale visual recognition. Developed in 2012 by Alex Krizhevsky in collaboration with Ilya Sutskever and his Ph.D. advisor Geoffrey Hinton at the University of Toronto, the model contains 60 million parameters and 650,000 neurons. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training. The three formed team SuperVision and submitted AlexNet in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points better than that of the runner-up. The architecture influenced a large number of subsequent work in deep learning, especially in applying neural networks to computer vision.

Web Search Results

• AlexNet

  AlexNet is a convolutional neural network architecture developed for image classification tasks, notably achieving prominence through its performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). It classifies images into 1,000 distinct object categories and is regarded as the first widely recognized application of deep convolutional networks in large-scale visual recognition. [...] The ImageNet dataset, which became central to AlexNet's success, was created by Fei-Fei Li and her collaborators beginning in 2007. Aiming to advance visual recognition through large-scale data, Li built a dataset far larger than earlier efforts, ultimately containing over 14 million labeled images across 22,000 categories. The images were labeled using Amazon Mechanical Turk and organized via the WordNet hierarchy. Initially met with skepticism, ImageNet later became the foundation of the [...] AlexNet is highly influential, resulting in much subsequent work in using CNNs for computer vision and using GPUs to accelerate deep learning. As of early 2025, the AlexNet paper has been cited over 172,000 times according to Google Scholar.

• The Story of AlexNet: A Historical Milestone in Deep Learning

  At its core, AlexNet is a convolutional neural network (CNN) comprising eight layers — five convolutional layers and three fully connected layers. The network processes visual data by extracting spatial hierarchies of features, starting with basic patterns like edges and shapes before identifying more complex structures such as textures or objects. Each convolutional layer applies filters (kernels) to input data, detecting features through sliding windows. Pooling layers reduce the spatial [...] In 2012, AlexNet revolutionized the field of computer vision and deep learning, marking a turning point in artificial intelligence (AI). Designed by Alex Krizhevsky, Ilya Sutskever, and their advisor Geoffrey Hinton, AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) that year, a global competition to identify and classify images with accuracy. While deep neural networks had existed for decades, AlexNet’s success demonstrated the power of deep learning and sparked a [...] Measurable Achievements: Dominating the ImageNet Challenge The measurable achievements of AlexNet are a testament to its significance in the field of AI. At the 2012 ILSVRC, AlexNet achieved a top-5 error rate of 15.3%, significantly outperforming the second-place model, which had a top-5 error rate of 26.2%. This dramatic improvement highlighted the potential of deep learning, inspiring researchers and engineers worldwide to explore convolutional neural networks further.

• Introduction to The Architecture of Alexnet

  A. AlexNet is a pioneering convolutional neural network (CNN) used primarily for image recognition and classification tasks. It won the ImageNet Large Scale Visual Recognition Challenge in 2012, marking a breakthrough in deep learning. AlexNet’s architecture, with its innovative use of convolutional layers and rectified linear units (ReLU), laid the foundation for modern deep learning models, advancing computer vision and pattern recognition applications. Q2. Why AlexNet is better than CNN? [...] A. AlexNet is a specific type of CNN, which is a kind of neural network particularly good at understanding images. When AlexNet was introduced, it showed impressive results in recognizing objects in pictures. It became popular because it was deeper (had more layers) and used some smart tricks to improve accuracy. So, AlexNet is not better than CNN; it is a type of CNN that was influential in making CNNs popular for image-related tasks. Q3. What are the advantages of AlexNet? [...] 8 layers: 5 conv, 3 pooling, 2 FC, 1 softmax ReLU activation, overlapping pooling, data aug GPU acceleration Pioneering CNN Q5. What is AlexNet good for? AlexNet is an early CNN for image classification. It was a significant breakthrough in 2012. Shipra Saxena Shipra is a Data Science enthusiast, Exploring Machine learning and Deep learning algorithms. She is also interested in Big data technologies. She believes learning is a continuous process so keep moving.

• ML | Getting Started With AlexNet

  # ML | Getting Started With AlexNet Last Updated : 12 Jul, 2025 Suggest changes 3 Likes AlexNet is a deep learning model that made a big impact in image recognition. It became famous for its ability to classify images accurately. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 with a top-5 error rate of 15.3% (beating the runner up which had a top-5 error rate of 26.2%). Most important features of the AlexNet are:

• AlexNet: A Revolutionary Deep Learning Architecture

  #### Share #### Subscribe to the viso blog Stay connected with viso.ai and receive new blog posts straight to your inbox. Subscribe AlexNet is an Image Classification model that transformed deep learning. It was introduced by Geoffrey Hinton and his team in 2012 and marked a key event in the history of deep learning, showcasing the strengths of CNN architectures and its vast applications. [...] Skip to main content Skip to footer Request a demo On this page #### AlexNet: A Revolutionary Deep Learning Architecture Subscribe Deep Learning # AlexNet: A Revolutionary Deep Learning Architecture AlexNet is a Image Classification model released in 2012 and the first model to use CNN based Deep Neural Network. Nico Klingler April 29th, 2024 On this page #### Subscribe to our newsletter Stay connected with viso.ai and receive new blog posts straight to your inbox. Subscribe [...] AlexNet marked a significant milestone in the development of Convolutional Neural Networks (CNNs) by demonstrating their potential to handle large-scale image recognition tasks. The key innovations introduced by AlexNet include ReLU activation functions for faster convergence, the use of dropout for controlling overfitting, and the use of a GPU to feasibly train the model. These contributions are still in use today. Moreover, the further models developed after AlexNet took it as a base

• Instance Of

  Q7397

AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor. AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.