Saliency detection is finding the visually significant and attention grabbing objects present in an image. The present work is about finding saliency detection methods using Multi-Stream Convolution Neural Network. The main aim of this is to train a CNN model which captures the contextual information and multiscale features. Different metrics like f-measure, recall, precision and MAE are used to know how our model is performing with respect to other models. We also used cross dataset evaluation to know how our model is performing with unknown data to know the generalization capabilities. We compared our results with other well-known methods such as IT, MZ and SR which proves the efficacy of our work.