Compressed residual-VGG16 CNN model for big data places image recognition

Deep learning has given way to a new era of machine learning, apart from computer vision. Convolutional neural networks have been implemented in image classification, segmentation and object detection. Despite recent advancements, we are still in the very early stages and have yet to settle on best practices for network architecture in terms of deep design, small in size and a short training time. In this paper, we address the issue of speed and size by proposing a compressed convolutional neural network model namely Residual Squeeze VGG16. Proposed model compresses the earlier very successful VGG16 network and further improves on following aspects: (1) small model size, (2) faster speed, (3) uses residual learning for faster convergence, better generalization, and solves the issue of degradation, (4) matches the recognition accuracy of the non-compressed model on the very large-scale grand challenge MIT Places 365-Standard scene dataset. In comparison to VGG16 the proposed model is 88.4% smaller in size and 23.86% faster in the training time. This supports our claim that the proposed model inherits the best aspects of VGG16 and further improves upon it. In comparison to SqueezeNet our proposed framework can be more easily adapted and fully integrated with the residual learning for compressing various other contemporary deep learning convolutional neural network models Broader impact of our work could improve the performance in specialized tasks such as video-based surveillance, self-driving cars, and mobile GPU applications.