The application relates to the technical field of deep learning, and provides a model construction method and device, an image processing method and device, a hardware platform and a storage medium. The model construction method comprises the steps that a neural network model used for image processing is trained, wherein the neural network model comprises at least one depth separable convolution module, and each depth separable convolution module comprises a layer-by-layer convolution layer, a point-by-point convolution layer, a batch normalization layer and an activation layer which are connected in sequence; and the trained neural network model is quantized to obtain a quantized neural network model. According to the method, firstly, model parameters are quantified, so that the data volume of the parameters is effectively reduced, and the model is suitable for being deployed in NPU equipment. Secondly, the depth separable convolution module in the method is different from the depth separable convolution module in the prior art, and a batch normalization layer and an activation layer are not arranged between a layer-by-layer convolution layer and a point-by-point convolution layer, so that values of model parameters are distributed in a reasonable range, and the model parameters can be quantized with high precision.