Abstract: Product image search is an important application of mobile visual search in e-commerce. The target of product image search is to retrieve the exact product in a query image. The development of product image search not only facilitates people’s shopping, but also results in that e-commerce moves forward to mobile users. As one of the most important performance factors in product image search, image representation suffers from complicated image background, small variance within each product category, and variant scale of the target object. To deal with complicated background and variant object scale, we present a multi-scale deep model for extracting image representation. Meanwhile, we learn image similarity from product category annotations. We also optimize the computation cost by reducing the width and depth of our model to meet the speed requirements of online search services. Experimental results on a million-scale product image dataset shows that our method improves retrieval accuracy while keeps good computation efficiency, comparing with existing methods.