Since the best way to learn a new technology is by using it to solve a
problem, my efforts to learn PyTorch started out with a simple
project: use a pre-trained convolutional neural network for an object
recognition task. In this article, we’ll see how to use PyTorch to
accomplish this goal, along the way, learning a little about the library
and about the important concept of transfer learning.
WhilePyTorchmightnotbeforeveryone,atthispointit’s
impossibletosaywhichdeeplearninglibrarywillcomeoutontop,
andbeingabletoquicklylearnandusedifferenttoolsiscrucialto
succeedasadatascientist.
The complete code for this project is available as a Jupyter Notebook
on GitHub. This project was born out of my participation in the
Udacity PyTorch scholarship challenge.
. . .
Approach to TransferLearning
Our task will be to train a convolutional neural network (CNN) that
can identify objects in images. We’ll be using the Caltech 101 dataset
which has images in 101 categories. Most categories only have 50
images which typically isn’t enough for a neural network to learn to
high accuracy. Therefore, instead of building and training a CNN
from scratch, we’ll use a pre-built and pre-trained model applying
transfer learning.
The basic premise of transfer learning is simple: take a model trained
on a large dataset and transferits knowledge to a smaller dataset. For
object recognition with a CNN, we freeze the early convolutional
Predicted from trainednetwork
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