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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
MULTICLASS IMAGE CLASSIFICATION BASED ON INCEPTION-V3 TRANSFER LEARNING MODEL
Authors
Pooja, Monika Bhatnagar
Keywords
Convolutional neural network, data augmentation, deep learning, multiple food recognition, transfer learning.
Abstract
Transfer learning is the reuse of a pre-trained model for a new problem, it is very popular nowadays in deep
learning because it can train deep neural networks with relatively little data, and it is very useful in data science because of
most real problems., you don't have millions of data points marked to train these complex models. Let's take a look at what
transfer learning is, how it works, why and when to use it. Includes several resources for models that have been previously
trained in learning transfers for example, when you train the classifier to predict whether an image contains food, you can use
the knowledge gained during training to recognize drinks, for example, if you trained a simple classifier to predict, if the image
includes a backpack, you can use the knowledge gained by the model during training.
IJCRT's Publication Details
Unique Identification Number - IJCRT2208067
Paper ID - 223721
Page Number(s) - a505-a510
Pubished in - Volume 10 | Issue 8 | August 2022
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Pooja, Monika Bhatnagar, "MULTICLASS IMAGE CLASSIFICATION BASED ON INCEPTION-V3 TRANSFER LEARNING MODEL", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.10, Issue 8, pp.a505-a510, August 2022, Available at :http://www.ijcrt.org/papers/IJCRT2208067.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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