  Gravitational waves are ripples in the fabric of space-time that travel at
the speed of light. The detection of gravitational waves by LIGO is a major
breakthrough in the field of astronomy. Deep Learning has revolutionized many
industries including health care, finance and education. Deep Learning
techniques have also been explored for detection of gravitational waves to
overcome the drawbacks of traditional matched filtering method. However, in
several researches, the training phase of neural network is very time consuming
and hardware devices with large memory are required for the task. In order to
reduce the extensive amount of hardware resources and time required in training
a neural network for detecting gravitational waves, we made SpecGrav. We use 2D
Convolutional Neural Network and spectrograms of gravitational waves embedded
in noise to detect gravitational waves from binary black hole merger and binary
neutron star merger. The training phase of our neural network was of about just
19 minutes on a 2GB GPU.
