Case study imagenet competition

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Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
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Building an Image Classifier Using Pretrained Models With Keras

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Building an Image Classifier Using Pretrained Models With Keras

Journal of Big Data volume 2 , Article number: 1 Cite this article. Metrics details. Big Data Analytics and Deep Learning are two high-focus of data science.
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In this post, I'll discuss commonly used architectures for convolutional networks. As you'll see, almost all CNN architectures follow the same general design principles of successively applying convolutional layers to the input, periodically downsampling the spatial dimensions while increasing the number of feature maps. While the classic network architectures were comprised simply of stacked convolutional layers, modern architectures explore new and innovative ways for constructing convolutional layers in a way which allows for more efficient learning.
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Comments (2)

  1. [MEMRES-18]
    Ranjit S. 19.05.2021

    It is not like English Writing or Advanced Writing, rather it is Good With Words.

  2. Antonio G.
    [MEMRES-28]
    Antonio G. 21.05.2021

    Thank you so much <,33

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