The break-out of a fire should be determined rapidly in order to prevent material damage and human casualties. Traditional low-level feature-based methods have a high rate of false alarms and have low detection accuracy. To overcome these issues, deep learning models for fire detection at early stages during surveillance are used. Smaller convolutional kernels and fully connected layers of the deep learning models help to do classification effectively by reducing the computational requirements.