Recently, image captioning has received much attention from the artificial-intelligent (AI) research community. Most of the current works follow the encoder-decoder machine translation model to automatically generate captions for images. However, most of these works used Convolutional Neural Network (CNN) as an image encoder and Recurrent Neural Network (RNN) as a decoder to generate the caption. In this paper, we propose a sequence-to-sequence model that uses RNN as an image encoder that follows the encoder-decoder machine translation model, such that the input to the model is a sequence of images that represents the objects in the image. These objects are ordered based on their order in the captions. We demonstrate the results of the model on Flickr30K dataset and compare the results with the state-ofthe-art methods that use the same dataset. The proposed model outperformed the state-of-the-art methods on all metrics.