This is a python-based project classifying the ten different digits of Numeral System based on deep analysis of image samples contained in the popular MNIST dataset
The model implemented for the above task is a stacked convolutional neural network that encompasses many aspects of the popular CNN structures used predominantly in real world scenario
The model has used convolution layers, inception layer, max-pool layers
and fully-connected neural nets as well to gain higher accuracy over simple nets.
Ordered stack of different layers keeping in mind the unique function of each layer has helped the model achieve a best validaion accuracy of 99.35%
The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples.
The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The dataset is available at:
http://yann.lecun.com/exdb/mnist/
Five layers have been applied on the dataset, namely:
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ConvLayer-1 -
ConvLayer-2 -
Inception Layer -
ConvLayer-3 -
Max-pool layers -
Fully Connected Neural Nets -
Pictorial Representation of the model:
In this project, we have used the predefined classes ranging from 0-9
Train-Validation Curve is a popular method to helps us confirm normal behavioural characteristics of model over increasing number of epochs
The model has been trained over 20 epochs with batch_size of 100
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Model
Since the entire project is based on Python programming language, it is necessary to have Python installed in the system. It is recommended to use Python with version >=3.6.
The Python packages which are in use in this project are matplotlib, numpy, torch and torchvision. All these dependencies can be installed just by the following command line argument
pip install requirements.txt
python main.py