A library for implementing Gated Residual Attention CNN for image classification problems.
Project description
# resattention
A python library for implementing a Residual Attention Convolutional Neural Network and training it for image classification problems. This model supports multi-class classification and is easy to use both for training and testing purposes.
The code is Python 2 and 3 compatible.
# Installation
Fast install:
pip install resattention
For a manual install get this package:
$wget https://github.com/garain/resattention/archive/master.zip
$unzip master.zip
$rm master.zip
$cd resattention-master
Install the package:
python setup.py install
# Example
from resattention import models
import tensorflow as tf
model = models.AttentionResNetCifar10(shape=(32,32,3),n_classes=2,n_channels=3)#RGB images
model = models.AttentionResNetCifar10(shape=(32,32,1),n_classes=6,n_channels=1)#GrayScale images
model.compile(tf.keras.optimizers.Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
# Models supported
AttentionResNetCifar10
AttentionResNet92
AttentionResNet56
# Please cite this publication if this library comes to any use:
Garain, B. Ray, P. K. Singh, A. Ahmadian, N. Senu and R. Sarkar, “GRANet: A Deep Learning Model for Classification of Age and Gender from Facial Images,” in IEEE Access, doi: 10.1109/ACCESS.2021.3085971.
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