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Deep neural networks have demonstrated state-of-the-art performance for feature-based image matching through the advent of new large and diverse datasets. However, there has been little work on evaluating the computational cost, model size, and matching accuracy tradeoffs for these models. This paper explicitly addresses these practical metrics by considering the state-of-the-art HardNet model. We observe a significant redundancy in the learned weights, which we exploit through the use of depthwise separable layers and an efficient Tucker decomposition. We demonstrate that a combination of these methods is very effective, but still sacrifices the top-end accuracy. To resolve this, we propose the Convolution-Depthwise-Pointwise(CDP) layer, which provides a means of interpolating between the standard and depthwise separable convolutions. With this proposed layer, we can achieve an 8 times reduction in the number of parameters on the HardNet model, 13 times reduction in the computational complexity, while sacrificing less than 1% on the overall accuracy across theHPatchesbenchmarks. To further demonstrate the generalisation of this approach, we apply it to the state-of-the-art SuperPoint model, where we can significantly reduce the number of parameters and floating-point operations, with minimal degradation in the matching accuracy.