As a classical computer vision perception task, pose estimation is commonly used in scenarios such as autonomous driving and robot grasping. The pose estimation algorithm based on template matching is advantageous in detecting new objects. However, current state-of-the-art template matching methods based on convolutional neural networks generally suffer from large memory consumption and slow speed. To solve these problems, this paper proposes a deep learning-based lightweight template matching algorithm. The method, which incorporates depth-wise convolution and the attention mechanism, drastically reduces the number of model parameters and has the capability to extract more generalized image features. Thus, the accuracy of position estimation for unseen and occluded objects is improved. In addition, this paper proposes an iterative rendering perspective sampling strategy to significantly reduce the number of templates. Experiments on open-source datasets show that the proposed lightweight model utilizes only 0.179% of the parametric quantity of the commonly used template matching model, while enhancing the average pose estimation accuracy by 3.834%.